Puccinia striiformis f. sp. tritici (Pst) resistance in a set of winter wheat strains mainly from southwestern China, and to identify DNA markers in these regions, we carried out a genome-wide association study (GWAS) of 120 China winter wheat accessions using single nucleotide polymorphism (SNP) markers from 90K wheat SNP arrays. In total, 16 SNP loci were significantly associated with wheat stripe rust in field and greenhouse trials. Of these, three distinctive SNPs on chromosomes 1B, 4A, and 6A were identified at a site in Mianyang in 2014, where the most prevalent wheat stripe rust races since 2009 have been V26 . This suggests that the three SNP loci were linked to the new quantitative trait loci (QTL)/genes resistant to the V26 races. Germplasm with immunity to Pst is a good source of stripe rust resistance for breeding, and after further validation, SNPs closely linked to resistance QTLs/genes could be converted into user-friendly markers and facilitate marker-assisted selection to improve wheat stripe rust resistance.Wheat stripe rust can cause considerable yield losses, and genetic resistance is the most effective approach for controlling the disease. To identify the genomic regions responsible for The online version of this article (10.1007/s13353-019-00533-8) contains supplementary material, which is available to authorized users. Puccinia striiformis f. sp. tritici (Pst), is one of the most destructive fungal diseases, causing significant grain yield losses under severe infections in wheat fields where high levels of moisture  and 47 temporarily named stripe rust resistance genes have been identified in wheat and its wild relatives  because it is virulent in wheat cultivars carrying the Yr26 gene. A recent study found that V26 is also highly virulent to the majority of Yr genes, except for Yr5, Yr15, and Yr61  mapping, genome-wide association studies (GWAS) detect statistical associations between phenotypic and genetic variations throughout the genome. With the development of the wheat 9K and 90K single nucleotide polymorphism (SNP) iSelect array technique, GWAS has been used increasingly to identify and dissect resistance genes or QTLs in common wheat. Using the 9K SNP iSelect array, Zegeye et al.  identifii et al.  analyzeda et al.  discovera et al.  used a su et al.  identifiu et al.  found thu et al. .In this study, we analyzed a set of 120 common wheat landraces mainly from Southwest China to evaluate their seedling stage and adult plant responses to stripe rust infection in multiple environments. We carried out GWAS based on the 90K SNP iSelect array. Our objectives were (1) to determine which genes or loci are associated with stripe rust resistance in this panel and (2) to identify SNP loci awaiting further exploitation in marker-assisted selection.Triticum aestivum L.) cultivars (lines) included in our study represent diverse cultivars that are cultivated in winter wheat regions of Southwest China  for 1 min, 72 °C for 1 min, and a final extension for 10 min at 72 °C. PCR products were mixed with 4 μl of the formamide loading buffer  and heated at 94 °C for 5 min. Each sample of 5 μl was loaded on 6% denaturing polyacrylamide gels and run at 100 W for about 2 h and then resolved by the silver staining method as described by Bassam et al. . The wheat lines Avocet S and Mingxian 169 were used as the susceptible control. Eighty seeds of each line were sown in 60-cm-wide paired-row plots, 1.2 m in length, with 30-cm row spacing and a 50-cm pathway between plots. Disease severity (DS) was classified as 0, 1%, 5%, 10%, 25%, 40%, 65%, and 100% throughout the season. We evaluated the adult plant stage, which is when the stripe rust reaction of the susceptible Mingxian169 cultivar was most severe. Infection types (ITs) 0-0, 1, 2, 3, and 4 were characterized as immune (IM), nearly immune (NIM), highly resistant (HR), moderately resistant (MR), moderately susceptible (MS), and highly susceptible (HS), respectively. Cultivars with the ITs of 0-0, 1, or 2 were classified as resistant, while those with the ITs 3 or 4 were classified as susceptible.Pst races  under controlled greenhouse conditions. The Pst races were maintained at the State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest Agriculture & Forestry University (China). Ten seeds of each accession were planted in a plastic seedling-raising plate  and grown in a growth chamber. Seedlings at the two-leaf stage (14 days after planting) were inoculated separately with urediniospores of each Pst. The inoculations were carried out by brushing conidia of isolates onto the seedlings to be tested, when the first leaf was fully expanded. Inoculated seedlings were then placed in plastic-covered cages and incubated at a temperature of 10 °C and relative humidity (RH) of 100% for 24 h. The seedlings were then transferred into a growth chamber under identical conditions, a daily day/night regime of 14 h light  at 17 °C and 10 h of darkness at 10 °C with 70% RH. The host responses to the stripe rust were recorded at least twice, approximately 18–21 days after inoculation, when the rust had fully developed on the susceptible control Mingxian 169. The disease resistance response was recorded by IT using the methods described above for adult plants  was analyzed using STRUCTURE software (version 2.3.4), with a burn-in length of 10,000 and 10,000 Markov chain Monte Carlo iterations for each k. Ten independent runs were carried out for each value of k. The maximum likelihood of each k value, the variance between the 10 runs, and the pedigree information of each line were weighted so that we could determine the optimal number of groups. The relative kinship (K) matrix was calculated using TASSEL 5.0.The population structure (r2) were calculated using the LD function according to Weir  was computed using 1000 permutations. The LDs were calculated separately for loci on the same chromosome  and unlinked loci .Linkage disequilibrium (LD) was identified using TASSEL 5.0 software. The allele frequency correlations , including both a Q matrix for fixed effects and a K matrix for random effects. The observed and expected P values for each trait (Q-Q plot) were used to compare the models and select the best model. Associations between SNP markers and stripe rust severity were evaluated using FarmCPU (fixed and random model circulating probability unification), an R package for GWAS, and genome prediction (http://zzlab.net/FarmCPU/index.html)   > 0.05 were selected and used for principal component analysis (PCA), linkage disequilibrium (LD), and GWAS . For the whole genome, the average r2 was 0.22. For each chromosome, the highest value of r2 (0.32) was on chromosome 4D, while the lowest (0.13) was on chromosome 6D. At the intra-chromosomal level, the LD decay based on the nonlinear regression of r2 with respect to genetic distance was approximately 12 cM in our population  (SP1-SP6)  Fig. . Accessip) was 4.69, a total of 16 SNPs were found to be significantly associated with the Pst resistance and were mapped to 11 regions of six chromosomes  mapping on 64.89 cM was significant in the greenhouse experiment (CRY32) and the three field trials . One SNP  was significant in a greenhouse experiment (CRY33) and two SNPs  were significant in the 2014GY field trial. The three SNPs were located within 5 cM of the SNP  and were most likely associated with a gene that was predicted to significantly affect Pst resistance in all experiments, except for that at the 2014MY site. A SNP on chromosome 2BS (Kukri_c55951_97) was found to be significant in two field trials (2014GY and 2014MY) and was mapped to the chromosome position of 19.6 cM.SNPs on chromosomes 1BL and 2BS were found to be significantly associated with Pst resistance in a single experiment, two SNPs, Tdurum_contig29087_628 and Tdurum_contig29087_757, which were mapped to 135.96 cM on chromosome 1BL, were significant in the 2014MY field trial. Another significant SNP  was mapped 28 cM away from the two SNPs mentioned above on the same chromosome. Three significant SNPs, BS00086365_51 (47.2 cM), RAC875_rep_c69619_78 (130.89 cM) and IAAV7287 (151.45 cM), were detected on chromosome 2A in two trials (2013GY and 2014GY) and in the greenhouse experiment (CRY32). On chromosome 2B, one SNP (BobWhite_c32319_313), mapped to position 134.46 cM, was significant in the 2013GY field trial. One significant SNP (BS00009637_51) was detected on chromosome 2D at the seedling stage with CRY32 inoculation, while another significant SNP (TA002369-0369) was identified on chromosome 4A in the 2014MY trial. On chromosome 6A, two SNPs, BS00079942_51 (71.24 cM) and wsnp_Ex_c965_1846161 (100.12 cM), were found to be significant in the 2013HZ and 2014MY field trials.Of the other nine chromosome regions that were significantly associated with Yr genes. For the correlation among the significant SNP loci, several significant correlations were observed, of which two SNPs on chromosome 1B (Tdurum_contig29087_628 and Tdurum_contig29087_757) were significant positive correlations with r value of 0.97, and two SNPs on different chromosomes (BS00086365_51 on 2A and Kukri_c55951_97 on 2B) were significant negative correlations with r value of 0.93. For the correlation among the significant SNPs and the markers closely linked to known Yr genes, markers linked to Yr15 (Barc8) and Yr26 (We173) were significantly associated with SNPs ; of which, markers (We173) linked to Yr26 and Excalibur_c18876_334 were significant positive correlations, with r value of 0.80 (Table 3).Correlation coefficients were calculated among the significant SNP loci obtained in above GWAS and the molecular markers of the known Pst pathogen (V26) was first identified in Sichuan province in 2009, because it was virulent to wheat cultivars carrying the Yr26 gene . The MLM model, which is based on Q and K matrices, proved to be a better fit and thus was selected for further association analysis  are within a few hundred bp to a few hundred kb  as well as natural infestation in three field trials . The SNP  was significant in the greenhouse experiment (CRY32) and the three environments, with an r2 value from 0.48 to 0.54. At present, two ASR genes have been reported on 1BL (YrExp1 and Yr26)  between the Yr26 linked marker we173 (1.4 cM) and the Excalibur_c18876_334, and we concluded that the SNP loci are most likely to closely link to gene Yr26. Another SNP (RAC875_c5227_1385) in this region also showed significant correlated with we173 at r value of 0.48 and suggested that it may be linked to Yr26 gene loci. In Mianyang, Sichuan province (2014MY), the prevailing Pst races were CRY32, CRY33, and V26; the two SNPs mentioned above were not detected because varieties carrying gene Yr26 has lost resistance to the V26 race. However, another region on chromosome 1BL, including two SNPs  at the same position (135.96 cM), was significantly associated with Pst resistance in the 2014MY field trials. Two genes (Yr29 and YrExp2) were reported on chromosome 1BL and confer APR to Pst races  on chromosome 1BL was significantly associated with Yr10 and Yr15 were both mapped to chromosome 1BS in previous studies  closely linked to Yr26. According to the results of correlation analysis, SNP (BS00067586_51) was not linked to genes Yr10 and Yr15, so it could be associated with a QTL for Pst resistance reported in this region . One very significant SNP  on chromosome 2BS was identified at two sites , while SNP  was significant at the site 2013GY. Strangely, correlation analysis showed the two SNPs showed significantly negative association at 33 Macer . HoweverYr51 and some QTLs have previously been mapped to the long arm of chromosome 4A  were basically the same prior to 2009, mainly comprising CYR32 and CYR33, but those in Mianyang in Sichuan province have mostly included CRY32, CRY33, and V26 since 2010  by using marker-assisted selection (MAS) in breeding programs. We also need to discover more resistance genes by transferring new genes from wild wheat relatives.The epidemic Our study demonstrates that most of Guizhou winter wheat accessions have high levels of stripe rust resistance, which could help to broaden the genetic resistance base of winter wheat breeding programs in southwestern China. For the accessions that we identified as carrying resistance to rust pathogens, further work is required to develop bi-parental populations of accessions to validate the resistance loci and develop user-friendly, tightly linked markers that can be employed to accelerate the incorporation of novel resistance into elite breeding wheat lines.ESM 1(DOCX 604 kb)ESM 2(XLSX 35 kb)


SNP data but are still being developed for genotyping‐by‐sequencing (GBS) data. In this study, we describe a method for using GBS data to predict gender using X and Y chromosomal SNPs. From a set of 1286 X chromosomal and 23 Y chromosomal deer (Cervus sp.) SNPs discovered from GBS sequence reads, a prediction model was built using a training dataset of 422 Red deer and validated using a test dataset of 868 Red deer and Wapiti deer. Prediction was based on the proportion of heterozygous genotypes on the X chromosome and the proportion of non‐missing genotypes on the Y chromosome observed in each individual. The concordance between recorded gender and predicted gender was 98.6% in the training dataset and 99.3% in the test dataset. The model identified five individuals across both datasets with incorrect recorded gender and was unable to predict gender for another five individuals. Overall, our method predicted gender with a high degree of accuracy and could be used for quality control in gender assignment datasets or for assigning gender when unrecorded, provided a suitable reference genome is available.Gender assignment errors are common in some animal species and lead to inaccuracies in downstream analyses. Procedures for detecting gender misassignment are available for array‐based  A set of tag pairs, a collection of sequence reads that differ by a single base pair in the genomic part of a read, were identified using Cervus elaphus) with some Red‐by‐Wapiti (Cervus canadensis) crosses, was used to build a prediction model, and a test dataset, consisting of 619 Red deer and 249 Wapiti, was used to validate the model, in which gender was recorded for all individuals. The animals were managed in accordance with the provisions of the New Zealand Animal Welfare Act 1999 and the Codes of Welfare developed under sections 68–79 of the Act. Tissue samples were collected using ear punches, and DNA was extracted according to Clarke et al.  using default settings, except that samples with a mean depth below 0.3 were discarded. Summary statistics of the filtered data are given in Table PYi, and the heterozygosity of the X chromosomal SNPs, Hi, were computed. Heterozygosity was adjusted for read depth, using ideas from Dodds et al. (Hi=ni/∑j=1Mx(1−2Kij), where ni is the number of heterozygous genotypes for individual i, Kij=1/2dij and dij is the read depth at SNP j in individual i for j = 1, … , Mx. Plots of MyPYi against Hi, which we refer to as gender plots, were produced, where My is the number of (filtered) Y chromosomal SNPs. The R code and the data used in this analysis are available at https://github.com/AgResearch/GBS_Gender_Predict.The samples for the test and training datasets with the filtered allosomal SNPs were run through s et al. , to accox‐axis. A prediction model was built using the empirical results from the training set such that an individual was classified male if PYi>20Hi2+0.2 or female if PYi<0.1+Hi, otherwise gender was unassigned. Under this model, 98.6% of the predicted gender matched the recorded gender. Four recorded females were classified as male and one recorded male classified as female. The gender of these individuals was identified as phenotypic misassignments on re‐examination, with the prediction model correctly classifying each individual. The gender plot for the test dataset  and sensitivity  of 99.8% and 100% respectively. These measures are similar to those obtained using SNP chip arrays  that result in males having non‐zero heterozygosity estimates for X chromosomal SNPs and reads to be called on the Y chromosome for females. Nevertheless, by combining information from the X and Y chromosomes, sufficient differentiation between males and females was obtained in order to predict gender accurately.One advantage of our method is that it accounts for errors from heterozygous genotypes being called as homozygous in the calculation of heterozygosity, which allows gender prediction on samples with low sample depth. Nevertheless, we discarded individuals with a sample depth below 0.3 because extremely low sample depth can lead to very few or no reads being observed for Y chromosomal SNPs. This would result in males having unassigned gender or even being classified female, as only a few Y chromosomal SNPs were discovered. This problem could be mitigated by using additional sequence reads without SNPs that align to the Y chromosome. However, this raw sequence information is often not available to the end user.et al. et al. et al. PYi, and Hi would be expected to equal zero. Another possibility is that sample contamination may lead to unexpected values of PYi and Hi, although this would depend on the type and level of contamination present. Consequently, our method could be utilised to detect aneuploidies or contaminated samples. Without any known abnormal animals or samples available in our collection, this could not be tested.For some individuals, points on the gender plot may fall outside the male and female regions. This may happen if the animal's sample depth is low, as errors can bias heterozygosity estimates for males. In our analysis, all but one of the individuals with unassigned gender were males with a sample depth around 0.6 or lower. Alternatively, these points may represent sex chromosome aneuploidies, such as males with XXY (Laurie In conclusion, we have developed a method using GBS data that predicts gender in animals with a high degree of accuracy. Although developed in deer, our method can be readily applied to other animal species with an XX/XY sex determination system, provided that a suitable reference genome with X and Y chromosomes is available.Figure S1 Proportion of heterozygous genotypes for males in the training dataset for X chromosomal SNPs relative to their position on the X chromosome.Click here for additional data file.


RAPGEF6) gene on chicken chromosome 13. A total of 1676 Chinese commercial Jing Hong laying hens—including two populations, P1 population (858 hens) and P2 population (818 hens)—were genotyped using the Polymerase Chain Reaction-Restriction Fragments Length Polymorphisms (PCR-RFLP) method for the association analysis of egg-laying rates for the verification of the GWAS results. Genotypic and allelic frequencies of five SNPs were inconsistent with Hardy–Weinberg equilibrium, and the average population genetics parameters considering all the SNP values; i.e., gene homozygosity (Ho), gene heterozygosity (He), the effective number of alleles (Ne), and the polymorphism information content (PIC) were 0.75, 0.25, 1.40, and 0.20 in P1; 0.71, 0.29, 1.46, and 0.24 in P2; and 0.73, 0.27, 1.43, and 0.22 in P1 + P2 populations, respectively. The association analysis results revealed that out of the five polymorphisms, three of them  had highly significant effects on egg-laying rates according to the GWAS results. Population-specific association analyses also showed similar significant association effects with this trait. Four haplotypes  were inferred based on significant loci (AX-75745340 and AX-75745341) and also showed significant associations with the egg-laying rate, where haplotype AAGG had the highest egg-laying rate, with the exception of the egg-laying rate in P1 population, followed by other haplotypes. Furthermore, genotypes TT, AA, and GG showed the highest egg-laying rate compared to the corresponding genotypes at AX-75745366, AX-75745340, and AX-75745341 SNP loci in P1+P2, respectively. A similar result was found in the population-specific analysis except for the P1 population, in which TC genotype showed the highest egg-laying rate. No significant association was found in the egg-laying rate during the 60 days laying period for the SNPs (AX-75745380 and AX-75745388) in any group of population (p ≥ 0.05). Collectively, we report for the first time that 3 SNPs in the RAPGEF6 gene were significantly associated with the egg-laying rate during the later stage of egg production, which could be used as the potential candidate molecular genetic markers that would be able to facilitate in the selection and improvement of egg production traits through chicken breeding.The improvement of egg production is of vital importance in the chicken industry to maintain optimum output throughout the laying period. Because of the elongation of the egg-laying cycle, a drop in egg-laying rates in the late laying period has provoked great concern in the poultry industry. In this study, we calculated the egg-laying rate at weeks 61–69 (60 days) of Jing Hong chickens parent generation as the phenotype, and the genotype were detected by the chicken 600K Affymetrix Axiom High Density (HD) Single Nucleotide Polymorphisms (SNP)-array. The Genome-Wide Association Study (GWAS) result showed that the egg production trait is significantly associated with five SNPs , which are in the rap guanine nucleotide exchange factor 6 ( Chicken eggs are rich in protein, fatty acids, vitamins, and minerals, and are measured as an excellent source of animal protein. With the enhancement of laying hens’ production performance, the laying age of commercial laying hens has been prolonged from the first 72–80 weeks, and some breeding companies have even-continued the laying cycle to 100 weeks, recommending the breeding program “breeding for 500 eggs in 100 weeks” . HoweverA good number of previous investigations have been identified several polymorphisms in different genes of laying chicken that are closely associated with egg production traits including egg number, laying rate, egg weight, and egg quality characteristics ,18,19,20RAPGEF6) is a protein-coding gene located on chromosome 13 in chickens (Gene ID: 101751943). Gene Ontology (GO) annotations associated with this gene consists of a guanyl-nucleotide exchange factor movement and a GTP-dependent protein requisite. The nucleotide sequence (15794705-15904202) specifies that this gene consists of 33 exons that encoded 109,498 amino acids (Accession No. NC_006100.3). The RAPGEF6 gene is preserved in transversely species comprising humans, chimpanzees, rhesus monkeys, dogs, cows, mice, rats, zebrafish, and Caenorhabditis elegans. Rap1, a member of the protein of the Ras-like superfamily of small GTPases, is involved in maintaining widespread cellular functions linked to cell adhesion, proliferation, differentiation, spreading, and endothelial junction control [RAPGEF6 gene has been established to perform a fundamental role in spermatogenesis [RAPGEF6 is essential for the reproductive development in mice. No polymorphisms in the RAPGEF6 gene that may affect economic traits in chicken have been investigated. Therefore, this study was planned to examine SNPs in the RAPGEF6 gene and their associations with the egg production trait to evaluate the effect of this gene on the egg-laying rate in chicken. This will provide further useful and detailed information that can be used to advance poultry breeding through the use of molecular marker-assisted selection programs.The rap guanine nucleotide exchange factor 6  and approved by the Biological Studies Animal Care Committee of Hubei Province, China as well as the ethics committee of Huazhong Agricultural University, Wuhan, China (Permission number: 4200896859).A total of 1676 Chinese Jing Hong laying chicken from two populations were used for genotyping and marker-trait association analysis under this study. The first population (P1), comprising 858 healthy Chinese Jing Hong laying chickens (61–69 weeks old), were obtained from the poultry farm Jingzhou Yukou Poultry Industry Co. Ltd., Jingzhou-434020, Hubei, China. The other population (P2), comprising a total of 818 Chinese Jing Hong laying chickens , were reared at Huadu Yukou Poultry Industry Co. Ltd., Beijing, China. These birds were raised individually in stair-step cages under a consistent environment with separated feed trays and egg collecting trays. The birds were fed by limited nutrition according to the percentage of their weights. The birds were fed a commercial corn-soybean-based iso-caloric and iso-nitrogenous diet, containing 2850 Kcal ME/kg dry matter (DM) and 16.83% crude protein (CP). The required feed was supplied twice a day, at 7:00 a.m. and at 4:00 p.m., according to the Chinese Jing Hong laying chicken rearing manual. Safe drinking water was also made available at all times by a nipple drinker. All birds were kept in identical light/dark cycles that exposed them to 16 h of light per day.The number of eggs produced from the individual experimental bird was recorded daily for 60 days duration (weeks 61–69). The egg-laying rate was calculated from data concerning the number of eggs produced by each hen using the following equation:RAPGEF6 gene of chicken with other species, we also downloaded nucleotide sequences of different species from the National Centre Biotechnology Information (NCBI)  and kept chicken as a reference. All collected sequences and their corresponding nucleotide sequences were aligned, and a phylogenetic tree was constructed using Molecular Evolutionary Genetics Analysis (MEGA) version 6 software with the maximum likelihood method [RAPGEF6 gene were compared with 16 species and the genetic similarity results were recorded.To construct the evolutionary relationships of the d method . The anaBlood samples were collected from the wing vein in a tube containing ethylene diamine tetra acetic acid (EDTA), as an anticoagulant. All samples were collected in an ice box and subsequently preserved at −20 °C until further use. The genomic DNA was extracted according to the phenol-chloroform method as described by Sambrook and Russel, 2006 , with sop = 2.09 × 10−7) and (p = 4.18 × 10−6), respectively. The two statistical models concurrently identified five genome-wide significant SNPs (p < 0.05) on egg production traits in these chickens. The global view of p-values for all SNP markers was visualized using a Manhattan and Quantile-Quantile (Q-Q) plot that was constructed for each trait using the “CM plot” package in the R software (version 3.5) [p value threshold was determined by the “LD-adjusted” Bonferroni method [RAPGEF6 gene.Jing Hong parent hens showing extreme performance differences were used to detect SNP markers associated with egg-laying rates using the GWAS. A high-density SNP array was employed here to identify associated variants underlying egg production traits using the chicken 600K Affymetrix Axiom HD SNP-array  to investigate whether the effects of these QTLs were associated with egg production traits . The GWAion 3.5) . A GWAS ion 3.5)  carried ion 3.5) . The geni method . Those fRAPGEF6 gene. Tabular description of five identified Database SNPs in the RAPGEF6 gene is shown in RAPGEF6 gene  reference sequence was used to design primers to amplify DNA fragments that contain those SNPs with the Oligo6 software. Primer synthesis was completed using Oligonucleotide synthesis technology of Sangon Biotech Co., Shanghai, China. A detailed description of primer sets and their corresponding products size with the location is that were used to genotype five tag SNPs is shown in −1), 10 × buffer 1.0 µL, dNTP 0.1 µL, rTaq 0.1 µL, and 7.2 µL sterile distilled water used for PCR. The amplification program consisted of an Eppendorf thermal cycler that was programmed for an initial incubation temperature at 95 °C for 5 min, followed by 35 cycles each of which included denaturing at 95 °C for 30 s, annealing at 50–55 °C  agarose gel electrophoresis and stained with ethidium bromide. The genotype pattern was recorded by the gel documentation system (BIO-RAD Image Lab) to check the genotype category of each chicken considering each SNP’s position. The ingredients of SNPs in the RAPGEF6 gene PCR-RFLP optimized Restriction Enzyme (RE) digestion mixture is shown in All experimental birds (P1 and P2) were then genotyped considering the identified SNP’s location following the PCR-RFLP technique. A total of five primers were designed for the genotyping of 5 tag SNPs in the 50–55 °C  for 30 sRAPGEF6 gene were analyzed by Microsoft Excel (MS Excel). Gene frequencies were calculated from genotypic frequencies. The test for the Hardy-Weinberg–equilibrium at each SNP site was conducted separately for the P1, P2, and P1 + P2 population, using the Haplo view version 4.2 software (http://www.broad.mit.edu/mpg/haploview/) [Ho), heterozygosity (He), the effective number of alleles (Ne), and the polymorphism information content (PIC) were statistically analyzed using the POPGENE v. 1.32 software [The genotype and allele frequencies at each SNP site were calculated for all populations according to electrophoresis results of the genotype categorization. Genotypic frequencies of different PCR-RFLP patterns were estimated from the combination of different RFLP alleles generated based on the presence or absence of one or more restriction sites. Different genotypes were identified based on different patterns. Individual Jing Hong layer chickens were genotyped for the five SNPs at base positions of 15836649, 15843452, 15829057, 15845449, and 15829303 and termed SNPs; SNP-01 (T-15836649-C), SNP-02 (C-15843452-T), SNP-03 (A-15829057-G), SNP-04 (G-15845449-A), and SNP-05 (A-15829303-G). Genotype frequencies of the SNPs in the ijk is phenotypic value of the target trait , μ is population mean of egg- laying rate, Li is fixed effect of the line, Gj is fixed effect of the SNP genotype or haplotype, Fk is random effect of the family, and eijk is the overall error term.Association analyses of the five SNPs genotypes or haplotypes with Phenotypic data of egg-laying rate were performed in the Chinese Jing Hong layer chicken population using the General Linear Model Procedures (GLM) of the SAS statistical 9.2 software package  . Data weType III sum of squares was used in each test. The threshold for significance was set at p < 0.05 and for high significance at p < 0.01. All values are presented as least square means with standard errors of the mean (LSM ± SEM). Means were compared for significant differences using Duncan’s Multiple Range Test [nge Test .p values for all SNP markers was visualized by a Manhattan plot, as shown in p value) in the Q-Q (Quantile-Quantile) plot  base pairs flanking the associated SNPs that were chosen for analysis. A tabular description of five identified database SNPs in the RAPGEF6 gene is shown in The global view of le) plot , suggestRAPGEP6 gene of chicken , t emperor penguin (Aptenodytes forsteri), Adelie penguins (Pygoscelis adeliae), Golden Eagle (Aquila chrysaetos canadensis), bald eagle , common pigeon (Columba livia), swan goose (Anser cygnoides domesticus), mallard duck (Anas platyrhynchos), society  finches (Lonchura striata domestica), hooded crow (Corvus cornix cornix), american crow (Corvus brachyrhynchos), ground tit (Pseudopodoces humilis), eurasian blue tit (Cyanistes caeruleus), great tit (Parus major), Japanese quail (Coturnix japonica), melmeted guineafowl (Numida meleagris), and wild turkey  were aligned, and a phylogenetic tree was constructed to know the evolutionary relationship with other species using MEGA version 6 software  with the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method. The analysis results of the chicken RAPGEF6 gene were compared with 16 species, and the genetic similarity results were recorded. The UPGMA phylogenetic tree showed that the chicken RAPGEP6 gene was closely related to the RAPGEP6 gene of the guineafowl (Numida meleagris) than that of other studied species  and two genotype pattern in SNP AX-75745380 (CC and CT), AX-75745340 (AA and AG), AX-75745388 (GG and AA) and AX-75745341 (AG and GG) (RAPGEF6 gene are presented in H1 (TAG), H2 (CAG), and H3 (TAA), which accounted for 94.77, 86.06, and 90.55% of the genetic information in P1, P2, and P1 + P2 populations, respectively. Haplotype H1 (TAG) was predominant in all groups of the population studied. Another haplotype reconstruction was performed based on two significant SNPs-SNP-03: AX-75745340 (A15829057G) and SNP-05: AX-75745341 (A15829303G)-according to these genotyping data, and four haplotypes  were identified among the individuals. For the P1 population, the haplotype present at the highest frequency was the AAGG haplotype (0.84), with the AAAG haplotype is the next most frequent (0.07), followed by AGAG (0.06), and AGGG haplotype is the lowest frequent (0.03). For the P2 population, the haplotype present at the highest frequency was the AAGG haplotype (0.65), with the AGAG haplotype being the next most frequent (0.14), followed by AAAG (0.13), and finally the AGGG haplotype as the lowest frequent (0.08). For the P1 + P2 population, the haplotype present at the highest frequency was the AAGG haplotype (0.75), with the AAAG haplotype is the next most frequent (0.1), followed by AGAG (0.09), and AGGG haplotype is the lowest (0.06).The genotype patterns of the SNPs in the  and GG) . The genRAPGEF6 gene are presented in p ≥ 0.05), with the exception of the P1 population in the SNP site AX-75745340 and AX-75745341 between concern alleles in the RAPGEF6 gene. This shows a low genetic diversity in the population, which might result from the cause of selection. As shown in Ho) was higher than gene heterozygosity (He) for all of the locus as well as for all of the population, with the effective allele numbers (Ne). The value of polymorphism information content (PIC) was not higher for all of the locus as well as for all of the population. The Hardy–Weinberg equilibrium and the average population genetics parameters considering all the SNPs  were 0.75, 0.25, 1.4, and 0.2 in P1; 0.71, 0.29, 1.46, and 0.24 in P2; and 0.73, 0.27, 1.43, and 0.22 in P1 + P2 populations, respectively. The PIC was not higher and the value of PIC for He in all groups of the population were 0.1074, 0.2077, and 0.1606, respectively. The genotypic and allelic frequencies of each identified SNP in the RAPGEF6 gene in Chinese local Jing Hong layer chickens. The association analysis results were shown between the SNP genotypes in the RAPGEF6 gene and egg-laying rate (LR) by 60-day laying period in the Chinese Jing Hong layer chickens, and the least square means and standard error of means (LSM ± SEM) of different genotypes for each SNPs are listed in p < 0.0001) association with 60 days egg-laying rate were found at the loci AX-75745366 (T15836649C), AX-75745340 (A15829057G), and AX-75745341 (A15829303G) in all populations of Chinese local Jing Hong layer chicken, which are treated as SNP-01, SNP-03, and SNP-05 respectively. The SNP-01, SNP-03, and SNP-05 was significantly associated with the 60 days of egg production. In the SNP AX-75745366 (T15836649C), the significantly highest (p < 0.0001) egg-laying rate was shown in chickens with genotypes TC (80.50 ± 0.86), followed by the genotypes TT (78.63 ± 1.08), and were the lowest with genotypes CC (72.61 ± 1.62) in P1 population. The genotypes TT (85.30 ± 1.02 and 81.86 ± 0.75) showed the highest egg-laying rate in P2 and P1 + P2 population, followed by the genotype TC (80.49 ± 0.79 and 80.49 ± 0.59), and was the lowest with genotypes CC (76.40 ± 1.54 and 74.42 ± 1.13), respectively. In the SNP AX-75745340 (A15829057G), the significantly higher (p < 0.0001) egg-laying rate was found in chickens with the genotypes AA  in P1, P2, and P1 + P2 population compared with the genotype AG , respectively. In the SNP AX-75745341 (A15829303G), the significantly highest (p < 0.0001) numbers of egg production were seen in chickens with the genotypes GG  in P1, P2, and P1+P2 population compared with genotype AG , respectively. There was no significant association for the SNP AX-75745380 (C15843452T) and AX-75745388 (G15845449A) for the egg-laying rate for the studied 60-day laying period (p ≥ 0.05).Statistical analyses were performed to test the significance of the difference of genotype effect on egg production performance among the five SNPs of p < 0.0001) egg-laying rate (83.03 ± 2.33) for the 60-day laying period, followed by haplotype AAGG (79.79 ± 0.40) and AGGG (75.85 ± 3.24); the lowest levels were associated with haplotype AGAG (59.07 ± 2.62). However, in case of P2, among the four haplotypes, haplotype AAGG was shown to be markedly associated with the significantly highest (p < 0.0001) egg-laying rate (84.50 ± 0.70) for the 60-day laying period, followed by haplotype AGGG (80.46 ± 2.07) and AAAG (77.27 ± 1.56); the lowest levels were associated with haplotype AGAG (71.90 ± 1.51). Finally, in case of P1+P2 population, among the four haplotypes, haplotype AAGG was found to be correlated with the significantly highest (p < 0.0001) egg-laying rate (81.76 ± 0.49) for the 60-day laying period, followed by haplotype AAAG (79.28 ± 1.34) and AGGG (78.94 ± 1.81); the lowest levels were associated with haplotype AGAG (68.23 ± 1.36).An association analysis between the haplotypes  inferred based on two significant loci—AX-75745340 (A15829057G) and AX-75745341 (A15829303G)—and the egg-laying rate by day 60 in P1, P2, and P1 + P2 population are shown in RAPGEF6 gene from chicken populations  and members of the transforming growth factor beta (TGFβ) superfamily, including growth differentiation factor-9 (GDF9), have been intensively studied in chickens [RAPGEF6 gene. To identify novel DNA markers associated with egg production traits in chickens, we examined polymorphisms in RAPGEF6 and evaluated their associations with egg production traits in Chinese Jing Hong laying hens.The genetic factors are destined to play a pivotal role in promoting egg production traits to further improve this economically important resource. In addition to a large number of the members of the hypothalamic–pituitary–gonadal hormone (HPG) axis, a wide variety of local intra-ovarian factors have been shown to play a critical role in normal follicular development and oocyte maturation, including the EF6 gene ,52,53,54EF6 gene ,12,21. TEF6 gene ,55,56. Pchickens ,58,59. HRAPGEF6 is a protein-coding gene located on chromosome 13 in chickens, which shows an important role in the reproduction. Previous findings demonstrated that this gene is involved in the directing many vital processes including spermatogenesis [RAPGEF6 gene in mice was demonstrated to be essential for male and/or female fertility. Results of current study showed that the egg-laying rate is significantly associated with three SNPs AX-75745366 (T15836649C), AX-75745340 (A15829057G) and AX-75745341 (A15829303G) which are found in RAPGEF6 gene. Furthermore, four haplotypes  were detected and the association analysis of haplotypes showed that the polymorphisms of SNP AX-75745340 (A15829057G) and AX-75745341 (A15829303G) in RAPGEF6 gene are significantly associated with egg production traits in Chinese Jing Hong layer chickens. Therefore, these results also strongly support that the three currently identified SNPs that are significantly associated in the RAPGEF6 gene might serve as a possible potential candidate in molecular genetic markers to aid in the improvement of egg production traits to be used in the breeding technique of chicken. The chi-square test results demonstrated that allelic and genotypic frequencies for the SNPs in the RAPGEF6 gene were not in Hardy–Weinberg equilibrium (HWE). These results suggest that the allelic and genotypic frequencies of the five polymorphic sites in the RAPGEF6 gene of the Chinese Jing Hong layer chicken population do not remain constant from generation to generation due to the influence of selection, mate choice, migration, and mutation. SNPs were tested and demonstrated a remarkably genetic disequilibrium between alleles, which might change the population structure and genetic drift in the studied populations.ogenesis ,32. Thisogenesis ,32. MoreRAPGEF6 (p < 0.05). This shows a low genetic diversity in the population, which may mainly be due to the cause of selection. Moreover, gene homozygosity (Ho) was higher than gene heterozygosity (He) for the SNP-01, AX-75745366 (T15836649C), which was also found to be under genetic disequilibrium. The reason why this phenomenon occurring may be explained mainly by the following two aspects: (i) the mutation of allele T to allele C in this RAPGEF6 fragment was initially present in the original chicken population at a lower frequency; (ii) this substitution has occurred recently. Even if the allele T was correlated with the significantly higher egg production traits, a breeding purpose to enhance body weight and egg weight traits resulted in the presentation of the allele C, and the selection pressure was not enough to increase the frequency of allele C up to a higher level within a very limited number of generations. If this reason were true, the genetic disequilibrium is easily understood, as mentioned above.Furthermore, we demonstrated that for SNP-01, AX-75745366 (T15836649C), the frequency of allele T was predominantly higher than allele C and frequency of genotype TT, and TC was higher than that of genotype CC in the Jing Hong layer chicken of P1, P2, and P1 + P2 populations, but only after the whole population was found to exhibit a significant genetic disequilibrium between T and C alleles in p > 0.05), the P2 population did not show genetic equilibrium (p < 0.05), and the P1 + P2 population was found to exhibit significant genetic disequilibrium between A and G alleles in RAPGEF6 (p < 0.05). This shows a low genetic diversity in the population, which may be mainly due to the cause of selection. Additionally, gene homozygosity (Ho) was higher than heterozygosity (He) for the SNP-03: AX-75745340 (A15829057G) was shown to be under genetic disequilibrium. The main reason for this phenomenon may be a tight linkage of the allele A with either an advantageous allele or with an artificially selected and therefore economically desirable trait, such as higher egg production. Egg production traits of Chinese local Jing Hong populations have already been improved to enhance early sexual maturity, egg-laying numbers, and egg weight traits in the last decades. Another reason may be attributed to allele A being naturally one of the dominant alleles during genetic evolution, thus being more conserved and more common than other alleles in this population. It certainly cannot be ignored that the number of birds examined in this population was not enough to demonstrate the true event, and an extreme allele frequency was estimated as a result.For SNP-03, AX-75745340 (A15829057G), the frequency of allele A is higher than allele G, and the frequency of genotype AA was higher than that of genotype AG in the Jing Hong layer chicken of P1, P2, and in P1 + P2 populations. Although the P1 population showed significant genetic equilibrium (p > 0.05), the P2 population did not show genetic equilibrium (p < 0.05), and the P1 + P2 population was found to exhibit significant genetic disequilibrium between A and G alleles in RAPGEF6 (p < 0.05). This shows a low genetic diversity in the population, which may be mainly due to the cause of selection. Moreover, gene homozygosity (Ho) was higher than gene heterozygosity (He) for the SNP-05, AX-75745341 (A15829303G). This SNP was found to be under genetic disequilibrium, possibly due to allele G in RAPGEF6 being naturally one of the predominant alleles during genetic evolution and thus being more conserved and more common than other alleles in this population. Furthermore, it is possible that allele A or G may be tightly linked with either an advantageous allele or with an artificially selected economically favorable trait, such as higher egg production and egg weight. Hence, the homozygotes with genotype AA or GG were either promoted under natural selection pressures to be better adapted, or they were artificially selected for favorable agricultural attributes. In fact, the egg production traits of Chinese local Jing Hong populations have already been improved with the aim to enhance early sexual maturity, egg-laying numbers, and egg weight traits for the past six generations. In this local Chinese Jing Hong chicken breeding, the A or G alleles may be coincidently linked with one or more of the selected breeding traits, thus presenting a possible explanation for higher allelic frequencies. Additionally, it cannot be ignored that the number of birds examined in each population was not enough to demonstrate the true event, and an extreme allele frequency was estimated as a result.For SNP-05, AX-75745341 (A15829303G), the frequency of allele G was higher than allele A and the frequency of genotype GG was higher than that of genotype AG in the Jing Hong layer chicken of P1, P2, and P1 + P2 populations. Although the P1 population showed genetic equilibrium  and spanning 0 kb in block 1 only. In the case of the P2 population, three variants, AX-75745340A/G, AX-75745341A/G, and AX-75745366T/C, showed significant LD with each other with highly strong D’ (D’ = 93 and 83) and spanning 7 kb in block 1 only. Finally, in the case of the whole P1 + P2 population, two variants, AX-75745340A/G and AX-75735341/G, showed significant LD with each other with strong D’ (D’ = 69) and spanning 0 kb in block 1 only. The LD analysis revealed that three variants, AX-75745340A/G, AX-75745341A/G, and AX-75745366T/C, in the RAPGEF6 gene were significantly associated with the egg-laying rate.The results of the association study and the linkage disequilibrium (LD) analysis revealed that the three significantly associated SNPs were closely linked together in this region. Linkage disequilibrium (LD) plays a vital role in mapping genes that affect complex diseases and identifying association among genetic markers and functional genes . UnderstCollectively, the results of the present study strongly suggest that the three novel SNPs are associated with the egg-laying rate and are thus potential molecular markers for egg productivity in local Chinese Jing Hong chicken breeding.RAPGEF6 gene are the most potential candidate molecular genetic markers that can be used to improve the egg production traits in MAS programs. Furthermore, the RAPGEF6 gene is not only beneficial in males but can also be considered as one of the novel potential candidate gene in regulating egg production traits in females. Our present study therefore brings new insight on the RAPGEF6 gene function in Chinese Jing Hong chickens. This study not only provides the candidate genetic markers for a marker-assisted selection of Chinese Jing Hong hens, but also provides a basic knowledge for further studies on SNP detection on the RAPGEF6 genes in other chicken breeds and other animal species.In conclusion, five SNPs that are associated with egg-laying rate were selected by the GWAS in a parent population. Out of them, three SNPs were confirmed in two commercial populations with PCR-RFLP genotyping data and egg-laying rates. Based on the results of an association analysis, it has been shown that the T allele at SNP AX-75745366 (t15846449T>C), the A allele at SNP AX-75745340 (a15829057A>G), and the G allele at SNP AX-75745341 (g15829303A>G) in the Patent applying No: 201810428916.5 resulting from the work.


Haemonchus contortus, which is highly pathogenic in small ruminants. The aim of this study was to identifying single-nucleotide polymorphisms (SNP) that were associated with fecal egg counts (FEC) and could be used as markers to identify resistance to H. contortus in goats.Haemonchosis is a major economic problem in goat production in humid, tropical and subtropical regions. The disease is caused by an abomasal nematode, CIITA, ATP2A3, HSPA8, STAT5B, ESYT1, and SERPING1 genes were associated with FEC in goats with a nominal significance level of P < 0.05. Two missense mutation in the exon region of the caprine CIITA gene resulted in replacement of arginine with cysteine at position 9473550 (R9473550C) and aspartic acid with glutamic acid at position 9473870 (D9473870E). Chinese goat breeds had significantly higher FEC than Bangladeshi goat breeds within their respective genotypes. Polymorphism information content (PIC), effective allele number (Ne), and heterozygosity (He) were greatest for the STAT5B_197_A > G SNP locus in all goat breeds. Pairwise coefficients of linkage disequilibrium  revealed complete LD (r2 = 1) between significant SNP polymorphisms in CIITA and SERPING1 and strong LD (r2 = 0.93 and 0.98) between polymorphisms in HSPA8 and ATP2A3, respectively. Correlation coefficient (r) between FEC and body weight (BW) was significantly positive  but that between FEC and packed cell volume (PCV) was negatively significant  in the total population of goats. On the other hand, correlation coefficient (r) between BW and PCV was not significant in total population of goats. Association analysis revealed that haplotypes within ATP2A3, HSPA8, and SERPING1 were significantly associated with FEC. Quantitative real-time PCR revealed that the relative expression of mRNA was higher (P < 0.001) for resistant, compared to susceptible, groups of goats for all candidate genes except CIITA.Ten novel variants in the H. contortus.This study identified SNP markers that can potentially be used in marker-assisted selection programs to develop goat breeds that are resistant to The online version of this article (10.1186/s40104-019-0327-8) contains supplementary material, which is available to authorized users. Haemonchus contortus (H. contortus), haematophagous abomasal nematode parasite  was therefore applied before analysis  were calculated using PICcalculator an online program available at https://www.liverpool.ac.uk/~kempsj/pic.html [r2) were derived for the significant SNP loci using genotypes for all goats in this study and the default settings in the Heploview software. Descriptive statistics were estimated using the partition–ligation, combination–subdivision expectation maximization algorithm [Genotypic and allelic frequencies were determined for each candidate genes in each population . Correspon 3.2;) . Populatpic.html –35. Hepllgorithm  in Heplolgorithm , 37.y is the FEC, μ is the overall population mean, B and V are fixed effects of breed and location associated with the FEC trait, G is one of the 129 SNP which were tested for an association with FEC, and e is residual error [Relationships between SNP and FEC were tested using R software  and a lial error . Multipl-ΔΔCT method in SAS [t-tests and a significance level of P < 0.05 were used to compare gene expression in resistant and susceptible goats using Graph Pad Software Prism7 .Differences in gene expression between resistant and susceptible goats in the challenge trial were tested using the 2d in SAS , 41. StuH. contortus, resulting in an overall infection rate as 46.75%. Average infection rates were 68.9% for Chinese goat breeds and 19.4% for BBG goats. Least-squares means for FEC and PCV differed (P < 0.001) between goats from China and Bangladesh. Least-squares means for FEC and PCV also differed  among breeds, but not between locations, in China (Table P < 0.01), but did not differ from female goats in PCV; this result was consistent across countries In contrast to results for FEC, body weights were similar among breeds, locations, and sexes  had significant associations with FEC at a nominal significance level of P < 0.05. The rest of polymorphisms (119 polymorphisms in 78 genes) had non-significant associations with the studied phenotype (FEC) are presented in Additional file CIITA resulted in replacement of arginine with cysteine at position 9473550 and aspartic acid with glutamic acid at position 9473870 (Table STAT5B (A/G at position 41961106), two SNP in the 3´UTR of ATP2A3 (A/G at position 24358932 and A/C at position 24358402), and five SNP in various intron regions  and TT homozygotes at CIITA_481_A > T (0.98) were very high. Frequencies of GG, GG, and AA homozygotes were also relatively high at ESYT1_559_G > C, HSPA8_1024_A > G, and HSPA8_1064_A > G . Genotypic frequencies for SNP polymorphisms in STAT5B, SERPING1, and ATP2A3 were more balanced than those of the other genes considered in the study. Frequencies of the C allele at CIITA_161_C > T and T allele at CIITA_481_A > T were very high (0.99). Frequencies of the G allele at ESYT1_559_G > C and HSPA8_1024_A > G and the A allele at HSPA8_1064_A > G likewise exceeded 0.80, but frequencies of the most common allele at the remaining loci were moderate, ranging from 0.52 to 0.76  followed by CCB, BBG, ESB, and NJY goats. The lowest values for He, Ne, and PIC were observed for CIITA in all goat breeds. Values for He, Ne, and PIC were relatively high for SERPING1 and ATP2A3 in all goats breeds but were consistently moderate for the remaining loci  for all goat breeds. CIITA, HSPA8, and ESYT1 were categorized as having low genetic diversity (PIC < 0.25) for all breeds except CCB and NJY for HSPA8_1024_A > G and HSPA8_1064_A > G and YCW for ESYT1_559_G > C, which were classified as having medium genetic diversity  were significantly associated with FEC, with nominal significance levels of P < 0.05 to P < 0.01 (Table P < 0.01) associations were observed for HSPA8_1024_A > G, HSPA8_1064_A > G, SERPING1_312_C > T, SERPING1 615_G > T, and ATP2A3_680_A > C (Table P = 0.003) and TT and TC genotypes (P = 0.004) at SERPING1_312_C > T, between TT and GG genotypes (P = 0.002) and TT and GT genotypes (P = 0.005) at SERPING1_615_G > T, and between CC and AA genotypes (P = 0.012) at ATP2A3_680_A > C. Differences between some others genotypes also reached lower levels of significance (P < 0.05)  for each genotype for candidate genes with significant effects on FEC are presented in Table HSPA8 and CIITA in Bangladeshi goats.The FEC for Chinese goat breeds were higher than those of Bangladeshi goats , Bangladeshi goats  and also in total population of goats . On the other hand, Correlation coefficient between PCV and BW was non-significant in Chinese goats, Bangladeshi goats and total population of goats. The correlations between BW and FEC was significantly positive  in Chinese goats, but non-significant and negative (− 0.27) in Bangladeshi goats. In total population of goats, the correlation between BW and FEC was highly significant and positive  (Table Correlation coefficients Table  between 1) Table .Table 8Cr2) were determined for each pair of SNP. Linkage disequilibrium was present between HSPA8_1024_A > G and HSPA8_1064_A > G, CIITA_161_C > T and CIITA_481_A > T, SERPING1_312_C > T and SERPING1_615_G > T, and ATP2A3_150_A > G and ATP2A3_680_A > C . The SNP variants in the intronic region of HSPA8 and the 3´ UTR region of ATP2A3 also expressed strong LD  with FEC in YCW, NJY, and CCB goats. Associations with FEC (P < 0.05) were also observed for haplotypes in HSPA8 in NJY goats and SERPING1 in CCB goats. The CCB goats that were heterozygous for the CG and TC chromosome segments had lower FEC compared to those that were homozygous for the TT chromosome segment, but this relationship was not evident for the other breeds of Chinese goats. No significant difference (P > 0.05) were found between any of the haplotypes and FEC in BBG goats.Associations between 13 haplotypes at ed Table . HaplotyThe FEC increased gradually from day 21 through days 42 and was higher in susceptible goats compared to resistant goats during that period  and for ATP2A3 and STAT5B (both P < 0.0001)  acts as a Ca2+-regulated lipid-transfer protein and membrane-fusion regulator, which facilitates intracellular signaling and expression of immune responses to viruses [STAT5B have been associated with production and growth traits in chicken and cattle [ATP2A3 [DRB-1, IGF-1, IL2, IL13, and IFNG and GIN resistance in goats.Ten novel SNP in sistance . CIITA, n humans –48, labon humans , and a fn humans , chickenn humans , and shen humans . CIITA ps fishes . ATP2A3  cancers . Mutation humans . HSPA8 h in mice . Extende viruses . Polymord cattle , 53. The [ATP2A3 . However [ATP2A3 , 12, 13 PIC > 0.50, 0.25 < PIC < 0.50, and PIC < 0.25, respectively [CIITA in Black Bengal goats from Bangladesh. Similar or somewhat higher levels of genetic diversity were observed for Chinese goats compared to Bangladeshi, goats. These results indicated that there was adequate genetic diversity for selection to be effective in controlling GIN infections for all genes except, perhaps, CIITA. The SNP polymorphisms in CIITA resulted in amino acid substitutions in the gene product and therefore had potential to have a functional association with GIN resistance. Given the low frequency of the deleterious allele at CIITA, these polymorphisms may reflect the presence of rare, deleterious mutations. Three significant mutations were detected in the 3′ UTR regions of STAT5B and ATP2A3 and may alter gene regulation and impact gene expression through mechanisms that disrupt miRNA binding [Population genetic parameters can be used to characterize genetic diversity within and among populations. The PIC is an indicator of the extent of polymorphism in a population and the value of PIC has been classified as high, intermediate, and low levels of polymorphism for ectively , 53, 55. binding . Previou binding , 57–59. CIITA and the two polymorphisms in SERPING1 were each in complete LD (r2 = 1), and, within each gene, represented by only two unique haplotypes. High levels of LD were also observed for polymorphisms in ATP2A3 (r2 = 0.93) and HSPA8 (r2 = 0.98). Polymorphisms with r2 > 0.33 are generally considered to be in relatively strong LD [r2 values are large, haplotype analysis is preferred to the analysis of individual SNP variants [ATP2A3, HSPA8, and SERPING1 were significantly associated with FEC.Linkage disequilibrium (LD) plays a vital role in mapping genes that affect complex diseases and identifying association among genetic markers and functional genes . Understtrong LD  and commvariants . The LD CIITA in GIN-resistant, compared to GIN-susceptible, goats. Resistant and susceptible goats were identified by screening a large sample of goats exposed to natural GIN infection. Animals were then dewormed and re-infected with H. contortus. Results therefore addressed the consistency of indicators of parasite resistance and subsequent levels of gene expression in different infection cycles. Goats that had lower FEC, indicating greater resistance to infection, under natural grazing conditions appeared much more resistant, based on indicators of parasite resistance, and also had greater expression of five of the six candidate genes in response to a subsequent controlled GIN infection. The high repeatability for indicators of parasite resistance across infection cycles was consistent with results reported for Pelibuey hair sheep [The artificial challenge trial revealed significantly higher expression levels for all candidate genes except ir sheep , and difir sheep . The resH. contortus and other GIN in goats. Ten SNP within six candidate genes were associated with FEC and provided a suite of potential molecular markers for further study and possible use in screening individuals for resistance to H. contortus.Population genetic parameter, LD among SNP markers, identification of non-synonymous mutations in candidate genes, differences in relative gene expression between resistant and susceptible goats, and associations with FEC involving both individual SNP genotypes and SNP haplotypes can be used to advance our understanding of options to utilize selective breeding and molecular markers to improve resistance to Additional file 1:Table S1. Primer sequences and PCR conditions for genotyping SNP in caprine candidate genes. Table S2. Cycling condition for Touchdown PCR (TD-PCR) genotyping of SNP. Table S3. Primer information for measurement of expression of mRNA by quantitative reverse-transcriptase polymerase chain reaction. Table S4. Least-squares means and standard errors for FEC, PCV and BW for each SNP genotype in Chinese and Bangladeshi goats. Table S5. Results of association and multiple comparison test P-values of non-significant polymorphisms. (DOCX 57 kb)


GBS) analysis of short, 64‐base tag‐level haplotypes in 4657 accessions of cultivated oat, we discovered 164741 tag‐level (TL) genetic variants containing 241224 SNPs. From this, the marker density of an oat consensus map was increased by the addition of more than 70000 loci. The mapped TL genotypes of a 635‐line diversity panel were used to infer chromosome‐level (CL) haplotype maps. These maps revealed differences in the number and size of haplotype blocks, as well as differences in haplotype diversity between chromosomes and subsets of the diversity panel. We then explored potential benefits of SNP vs. TL vs. CL GBS variants for mapping, high‐resolution genome analysis and genomic selection in oats. A combined genome‐wide association study (GWAS) of heading date from multiple locations using both TL haplotypes and individual SNP markers identified 184 significant associations. A comparative GWAS using TL haplotypes, CL haplotype blocks and their combinations demonstrated the superiority of using TL haplotype markers. Using a principal component‐based genome‐wide scan, genomic regions containing signatures of selection were identified. These regions may contain genes that are responsible for the local adaptation of oats to Northern American conditions. Genomic selection for heading date using TL haplotypes or SNP markers gave comparable and promising prediction accuracies of up to r = 0.74. Genomic selection carried out in an independent calibration and test population for heading date gave promising prediction accuracies that ranged between r = 0.42 and 0.67. In conclusion, TL haplotype GBS‐derived markers facilitate genome analysis and genomic selection in oat.In a de novo genotyping‐by‐sequencing ( Avena sativa) is the sixth most important cereal crop. It is grown in temperate regions for grain, and in subtropical regions for forage. Both grain and forage are used for feed, while the grain provides a nutritional human food with documented health benefits  and similar methods  haplotypes, to differentiate them from the TL haplotypes that are inferred directly from GBS tags by Haplotag. In comparison with using individual SNPs, haplotype‐based analysis can reduce false discovery rates because it performs fewer association tests  genetic linkage mapping (TL haplotype vs. GBS‐SNPs), (ii) population genomics and haplotype mapping of elite North American lines, (iii) GWAS analysis of heading date (GBS‐SNPs vs. TL haplotype vs. CL haplotypes) and (iv) genomic selection using TL haplotype vs. GBS‐SNPs.de novo discovery mode of Haplotag, we called 164741 TL haplotype loci with 353130 TL alleles and 241224 GBS‐SNP markers from 4657 cultivated oat lines. These lines consisted of mapping population lines, breeding lines and germplasm material. To our knowledge, this is the largest number of cultivated oat lines that have been addressed in a single analysis. The complete data matrices and the supporting Haplotag input files can be downloaded in a set of annotated text files, while the complete set of genotype calls and map locations of the markers (see below) have been fully integrated into the T3/oat platform (http://triticeaetoolbox.org/oat/genotyping)  between the two 1 cM bin maps. The average number of markers per bin was 10.6 for the TL haplotype map and 14.9 for the GBS‐SNP map.The updated oat consensus map Appendix S1) contains a comprehensive set of 99878 mapped markers. This number includes the 74461 new Haplotag‐derived markers, and the complete set of markers that were reported by Chaffin et al. . A total containset al.  mapped to different groups, and 290 (4.6%) mapped to positions separated by more than 10 cM. As the algorithms used to place markers on the framework were identical, these discrepancies are most likely caused by the addition of three new mapping populations that were not included by Chaffin et al. . These pn = 635). Across all lines, the linkage disequilibrium (LD)‐based haplotype detection method  and southern (n = 123) sets of germplasm, respectively. The CL haplotype blocks in the full set covered 246 cM, while those in the southern set covered 573.9 cM and those in the spring set covered 521.2 cM. The genome‐wide average haplotype block sizes were 0.31, 0.29 and 0.43 cM  heading data from 16 location‐years. The first set compared two types of Haplotag‐derived markers (TL haplotype vs. GBS‐SNP) and was performed separately for each environment using the full diversity panel, as well as the spring and southern subsets. The second set compared Haplotag‐derived markers vs. CL haplotypes using BLUP values across environments using the full diversity panel (Table We conducted two sets of GWAS comparisons for heading date using the CORE diversity panel (el Table .10P ≥5.41) vs. only 69 GBS‐SNP associations (−log10P ≥5.55). GWAS conducted in the spring and southern sets showed the same trend, although the differences were smaller. The two chromosome representations with the most significant associations were Mrg02 and Mrg12. On Mrg02, there were 20 loci at position 34 cM associated with heading dates from eight field trials. On Mrg12, there were 23 TL haplotype markers at positions 40–42 cM associated with heading dates from seven locations (Appendix S3).The first GWAS identified 184 significant associations across the two marker systems after Bonferroni correction (Appendix S3). These analyses were conducted using 12890 TL haplotype (MAF ≥0.05) and 17694 GBS‐SNP (MAF ≥0.05) markers. These included 115 significant TL associations exceeding the 5% Bonferroni threshold (−logP = 0.05)  and applied a PCA‐based outlier detection method called ‘pcadapt’. The first step in this analysis identified the first nine principal components. K = 9 was selected because it appeared as the highest point before the beginning of a plateau at approximately K = 10  threshold (∞ ≤ 0.05). These loci were distributed across thirteen chromosome representations . The first cross‐validation comparison used random calibration sets comprising 40% to 80% of the diversity panel, with the remaining unselected lines used to make up the test sets. The mean cross‐validation accuracies of the GBS‐SNP and TL haplotype markers showed no statistically significant differences, and both reached a plateau at a calibration set size of 60%  as calibration set. Independent predictions were computed using heading dates from 16 environments as calibration set phenotypes. The prediction accuracies were calculated as the correlation between the predicted heading date values with the observed phenotype from Home‐test 2010 at Prince Edward Island, Canada . The genomic selection model using GBS‐SNPs gave slightly higher accuracy than the model using TL haplotype markers.An independent validation was performed using Home‐test 2010 lines as test set and the diversity set of lines , we found a high degree of similarity, with some differences in the identified genomic regions Figure . Surpriset al., et al., P) values . Unlike FST‐based methods, pcadapt does not require the prior grouping of individuals into subgroups  gave comparable cross‐validation accuracies. Similar results were obtained in maize cross‐validation, especially in traits with high genetic variance , of which seven were used in previous consensus map construction . A set of 197 ‘home‐test’ lines from the 2010 Ottawa oat breeding programme were also included to validate prediction accuracy of genomic selection. An additional set of 1248 lines from a public oat genotyping initiative (POGI) were included; however, data from these lines did not contribute to the reported genetic analyses. The POGI lines were included to expand the sampling of TL haplotypes, such that the current map and marker nomenclature would be directly applicable to future studies using this material.A total of 4657 cultivated oat lines from predominantly North American breeding materials were selected for GBS analysis Appendix . Three met al., DNA extraction and library preparation for the double digest (PstI‐MspI) GBS system were described in previous work  were used for marker placement (Appendix S2). TL haplotype and GBS‐SNP markers with a maximum of 50% missing values, >15% MAF and <10% heterozygosity were selected from the full data matrix. The genetic positions and the genotypes of the markers used for the oat consensus mapping , the spring lines (n = 497) and the southern set (n = 123) using the method described by Gabriel et al.  reported by Esvelt Klos et al. (K) and population structure (PCA) were accounted for in the mixed linear model (MLM) implemented in TASSEL version 5 , where n = the number of loci. Significant GWAS hits with deflated P values resulting from rare (frequency<1%) haplotypes, or heterozygotes were discarded.Genome‐wide genotype–phenotype associations were identified using two marker systems (SNP and TL haplotype) on the same set of CORE diversity lines −m), where i is number of individuals, b is number of haplotype blocks, k is the number of alleles and m is the number of major alleles  columns, and the haplotype incidence shows the probability that individual i carries a haplotype k. Individuals carrying the major haplotype 1 have 0 values in all the rows of that specific haplotype block. The R package ‘impute’ was used to impute the missing values of the incidence matrix. The incidence matrix was imported into TASSEL as a numeric marker. PCA and kinship matrix data generated using the respective TL haplotypes and GBS‐SNP markers were used for the parallel GWAS comparison of the TL haplotypes, GBS‐SNPs, CL haplotypes and a combined data set. The combined data set excluded markers that were components of the CL haplotype blocks.The CL haplotype blocks were converted to marker scores that represented the probability of the minor haplotype and imported into TASSEL. The CL haplotype blocks of the full set and their respective individual markers (TL haplotype and GBS‐SNP) were used to populate a CL haplotype incidence matrix with the dimension n = 635) with ≥5% MAF and ≤20% missing genotypes were imputed using the linkage disequilibrium‐based k‐nearest neighbour genotype imputation method, LD KNNi  outlier loci were identified after the P‐values were adjusted for false discovery rate or transformed to q‐values using the R package   was conducted by taking random samples of the population as a calibration set with the remainder used as a test set. The cross‐validation to determine the optimum calibration size was performed using 13947 TL haplotype or 20373 GBS‐SNP markers with 20% maximum missing markers. The missing marker scores were imputed by the EM algorithm implemented in rrBLUP, which is the recommended method for GBS markers  as a calibration set and the 2010 home‐test (n = 197) as the test set based on 13954 TL haplotype and 20380 GBS‐SNP markers.The ridge regression best linear unbiased prediction (RR‐BLUP) algorithm implemented in the R package (rrBLUP) was used for genomic prediction  TL‐haplotype loci and (b) GBS‐SNPs.Figure S2 Mean chromosome haplotype diversity of the three subpopulations.Figure S3 Haplotype diversity of the full set of lines (a), the spring set (b), and the southern set (c) infered using the TL‐haplotype markers.Figure S4 The screeplot shows the proportion of explained variances against the first 20 principal components.Figure S5 Panel (a) shows heading date prediction accuracies of the home test 2010 (n = 197) population data from Prince Edward Island, calculated using the calibration data from the CORE‐diversity set (n = 635) from 16 locations‐years, and panel (b) shows the predicted and observed heading date values using the BLUP calibration set.Table S1 GWAS comparisons.Table S2 Outlier genomic regions in the CORE (n = 635).Click here for additional data file.Appendix S1 The list of 4657 taxa genotyped and the consensus map.Appendix S2 The genotype data of the 950 lines used to update the consensus map (full data available by accessing haplotag.aowc.ca/SM3_OC_placement_RAW_merged.zip)Appendix S3 Significant GWAS hits of the different comparisons.Click here for additional data file.Appendix S4 The input file used for the Haplotag analysis of the 4657 lines.Click here for additional data file.Appendix S4 The input file used for the Haplotag analysis of the 4657 lines.Click here for additional data file.


Native cattle breeds are important genetic resources given their adaptation to the local environment in which they are bred. However, the widespread use of commercial cattle breeds has resulted in a marked reduction in population size of several native cattle breeds worldwide. Therefore, conservation management of native cattle breeds requires urgent attention to avoid their extinction. To this end, we genotyped nine Swedish native cattle breeds with genome-wide 150 K single nucleotide polymorphisms (SNPs) to investigate the level of genetic diversity and relatedness between these breeds.We used various SNP-based approaches on this dataset to connect the demographic history with the genetic diversity and population structure of these Swedish cattle breeds. Our results suggest that the Väne and Ringamåla breeds originating from southern Sweden have experienced population isolation and have a low genetic diversity, whereas the Fjäll breed has a large founder population and a relatively high genetic diversity. Based on the shared ancestry and the constructed phylogenetic trees, we identified two major clusters in Swedish native cattle. In the first cluster, which includes Swedish mountain cattle breeds, there was little differentiation among the Fjäll, Fjällnära, Swedish Polled, and Bohus Polled breeds. The second cluster consists of breeds from southern Sweden: Väne, Ringamåla and Swedish Red. Interestingly, we also identified sub-structuring in the Fjällnära breed, which indicates different breeding practices on the farms that maintain this breed.This study represents the first comprehensive genome-wide analysis of the genetic relatedness and diversity in Swedish native cattle breeds. Our results show that different demographic patterns such as genetic isolation and cross-breeding have shaped the genomic diversity of Swedish native cattle breeds and that the Swedish mountain breeds have retained their authentic distinct gene pool without significant contribution from any of the other European cattle breeds that were included in this study. Based on the type of livestock management, European cattle breeds can be broadly categorized into either commercial or traditional/native cattle. Commercial cattle breeds are mainly used in intensive animal farming, which aims at maximizing the overall production and economic profit, with a few popular breeds being disseminated throughout the world , 2. In cIn a recent FAO report , many caIn Sweden, the old local bovine breeds predominated until the twentieth century, although importation of some cattle from the Netherlands was documented as early as the sixteenth century . For exaSwedish native cattle breeds display a large phenotypic diversity in terms of conformation- and production-related traits , 15. ForWhile many studies have investigated the patterns of the genetic diversity and structure in traditional European cattle breeds , 7, 10, We genotyped samples from 147 individuals that represented nine Swedish cattle breeds  array, with an average SNP spacing of about 19 kb. Genotypes were called using the GenomeStudio® software  and two samples with a genotyping rate lower than 95% were discarded from the dataset. Furthermore, analysis of the pedigrees revealed that two samples had a foreign origin and were excluded from the dataset. A bash script was used to convert the Illumina genotypes report file to plink-file —“ped” and “map”—format. To ensure that only high-quality SNPs were included in the dataset, we excluded SNPs that were assigned to sex chromosomes and unassembled contigs, SNPs with missing genotypes in more than 5% individuals, and SNPs with a minor allele frequency (MAF) lower than 0.05. All quality-filtering steps were carried out using PLINK 1.9 [DNA samples from the 147 individuals were genotyped using the GeneSeekLINK 1.9 . Filteriadegenet [o) and the average expected heterozygosity (He). We also estimated the levels of runs of homozygosity (ROH), which are long stretches of identical-by-descent (IBD) homozygous genotypes that can provide valuable insight into recent and past demography of a population [homozyg-window-missing 2), two heterozygous SNPs along the entire ROH segment (–homozyg-het 2), and one or more SNPs per 80 kb region (–homozyg-density 80). In addition, the pattern of linkage disequilibrium (LD) decay between pairwise SNPs was generated for the breeds for which the sample included more than eight individuals to supplement the demographic pattern that was inferred based on ROH. For this purpose, we used SNeP [2 values between pairs of SNPs located within 2-Mb windows [To assess the genetic diversity in each breed, we used the R package adegenet  to estimpulation . For ROHsed SNeP  to calcu windows .Fst and construction of the maximum likelihood-based phylogenetic tree. To perform PCA, the “bed” format of PLINK was converted into “gds” format using the “gdsfmat” package before applying the “snpgdsPCA” function of the SNPrelate package [indep” function in PLINK (v 1.9) to reduce the overall pairwise LD to less than 0.15. The output from ADMIXTURE was visualized using the python package PONG [Fst distance was calculated using the “staMPP” R package [Fst distance matrix using the “phangorn” R package [To assess population structure among the nine Swedish native cattle breeds, first we used three approaches that consider individual SNPs: (1) principal component analysis (PCA), (2) admixture analysis, and (3)  package , 27. ADM package  was carrage PONG . Pairwis package . A neigh package . We also package .Finally, the genetic structure/clustering pattern was assessed using the algorithm implemented in CHROMOPAINTER and fineSTRUCTURE . Becausemind 0.9, –geno 0.05, –maf 0.05. Sample information for the final merged dataset that was used for the downstream analysis is in Additional file To investigate the relationships between the Swedish native cattle breeds and other European cattle breeds, we recovered bovine SNP genotyping array data for various European cattle breeds previously reported , 35–38 fm), ranging from 0.7981 in Väne to 0.9736 in Swedish Holstein-Friesian. This indicates that the SNPs selected for the GGD HD150K array are highly informative for Swedish cattle breeds. Similarly, the expected heterozygosity (He) is relatively high with values ranging from 0.3041 in the Väne breed to 0.3883 in the Swedish Holstein-Friesian breed.After applying the filtering criteria, the final dataset for the Swedish native breeds consisted of 143 samples and 111,914 SNPs. The genetic diversity summary statistics are in Table 2 values at all pairwise SNP distances and an overall slow LD decay. For the Fjäll, Swedish Holstein-Friesian and Swedish Red breeds, we inferred a wide haplotype diversity since they have the lowest r2 value at all pairwise SNP distances and a rapid LD decay.Demographic inferences in Swedish cattle breeds were made based on the analyses of ROH and LD decay. The ROH profile (ROH count and cumulative ROH length) varied a lot both between and within the Swedish cattle breeds Fig. b are conTo assess the genetic relationships among the Swedish native cattle breeds, first we performed a PCA. The first principal component (EV1), which explains 5.76% of the total variance, clearly separated the Fjäll, Swedish Polled, Fjällnära and Bohus Polled breeds from the Swedish Red, Väne and Ringamåla breeds. The second principal component (EV2), which explains 3.03% of the total variance, separated the Swedish Holstein-Friesian breed from the cluster of native Swedish cattle breeds. Furthermore, among the native Swedish cattle breeds, Swedish Red Polled  and Väne clearly form separate clusters .Haplotype sharing analysis by using ChromoPainter and fineStructure reveals a pattern Fig.  that notWe investigated the relationships between Swedish cattle breeds and other European cattle breeds through analyses based on PCA, ADMIXTURE, and phylogenetic trees. The other European cattle breeds mainly originated from north-western Europe, and Norway  indicate low genetic diversity in the Väne, Bohus Polled and Ringamåla breeds. Inferences drawn based on ROH pattern and LD-decay  is relatively large, and thus, they require special attention for conservation. Moreover, we also show that the Swedish Mountain cattle breeds (including Fjällnära) are unique in that they have maintained authentic local ancestry. Future studies should aim at genotyping a larger number of individuals using high-density genome-wide SNP arrays or whole-genome sequencing approaches, to help identify the genetic factors involved in their adaptive potential.Additional file 1: Table S1. Overview of the Swedish cattle breeds. Table S2. Information on the samples included from the previous studies. Table S3. Cumulative ROH count, ROH length (kb), and average ROH size (kb) per individual.Additional file 2: Figure S1.Fst-based phylogenetic tree showing the relationships between different European cattle breeds. The following abbreviations are used: JER-Jersey, GNS: Guernsey, LMS: Limousin, NOR: Normande, KC: Kerry cattle, RAN: Red Angus, GA: Galloway, HL: Scottish Highland cattle, DF: Dutch Friesian, SHF: Swedish Holstein-Friesian, HOL: Holstein-Friesian, VAC: Väne cattle, NRC: Norwegian Red cattle, SRC: Swedish Red cattle, RMC: Ringamåla cattle, SRP: Swedish Red Polled, FNC: Fjällnära cattle, SPC: Swedish Polled cattle, SMC: Swedish Mountain cattle (Fjäll cattle), BHP: Bohus Polled cattle.