Macrophomina phaseolina and Fusarium thapsinum are recognized as the major causal organisms. The diseases become more aggressive when drought and high-temperature stress occur during grain filling. Progress in genetic improvement efforts has been slow due to lack of effective phenotyping protocol and the strong environmental effect on disease incidence and severity. Deployment of modern molecular tools is expected to accelerate efforts to develop resistant hybrids. This study was aimed at identifying genomic regions associated with resistance to both causal organisms. A sorghum diversity panel consisting of 300 genotypes assembled from different parts of the world was evaluated for response to infection by both pathogens. Community resources of 79,132 single nucleotide polymorphic (SNP) markers developed on the panel were used in association studies using a multi-locus mixed model to map loci associated with stalk rot resistance. Adequate genetic variation was observed for resistance to both pathogens. Structure analysis grouped the genotypes into five subpopulations primarily based on the racial category of the genotypes. Fourteen loci and a set of candidate genes appear to be involved in connected functions controlling plant defense response. However, each associated SNP had relatively small effect on the traits, accounting for 19–30% of phenotypic variation. Linkage disequilibrium analyses suggest that significant SNPs are genetically independent. Estimation of frequencies of associated alleles revealed that durra and caudatum subpopulations were enriched for resistant alleles, but the results suggest complex molecular mechanisms underlying resistance to both pathogens.Stalk rots are important biotic constraints to sorghum production worldwide. Several pathogens may be associated with the disease, but  Sorghum is the world’s fifth most important cereal crop. It is a staple food for more than half a billion people in the world, 60% of whom live in Africa. As a C4 species, sorghum has very high yield potential comparable with maize and rice. However, numerous biotic and abiotic stresses pose formidable challenges to the realization of this potential. In addition to various cropping systems and management practices, host plant resistance and/or tolerance to these stresses have been widely recognized as the most viable approach to enhancing productivity under these conditions  was used in this study. The genotypes were planted in three environments in a randomized complete block design and replicated twice. The tests were conducted at the Kansas State University (KSU) Agronomy Research farm at Ashland Bottoms near Manhattan, Kansas, in the 2011 and 2012 seasons, and at the KSU North East Agricultural Experiment Station near Ottawa, Kansas, in the 2012 season. Soil at Manhattan (Ashland Bottoms) was a well-drained Smolan Silty Clay Loam, whereas the Ottawa soil is Harney Silt Loam. Plots were 6-m-long single rows spaced 0.75 m apart with a 0.6-m alley at the end of each plot. Three grams of seeds of each entry were drilled into the plots after treatment with herbicide safener  and the seedlings were later thinned to approximately 40 plants per plot. Fertilizer phosphorous [di-ammonium phosphate (DAP)] and nitrogen (urea) were applied at the rate of 34 kg PFusarium thapsinum and Macrophomina phaseolina, were selected for this study. Previous reports indicate that these two organisms are more virulent than other stalk-associated fungi and are most frequently associated with stalk rot diseases in sorghum  from a pure culture of the pathogen provided by Dr. Chris Little of the Department of Plant Pathology at KSU. The suspensions were incubated at room temperature on a rotary shaker until conidia were produced and then strained through four layers of cheesecloth to separate the conidia from the mycelial mass. The number of conidia in the suspension was determined by counting the spores under a microscope using a hemacytometer and the concentration was adjusted to 5 × 104 conidia ml−1 by diluting with 10 mM (pH 7.2) Phosphate-buffered saline and kept on ice until inoculation. At 14 d after flowering, approximately 1 ml of the suspension was injected into the basal stalks of the three red-tagged plants in each plot. An Idico filler-plug gun  equipped with a stainless steel needle similar to that described by For M. phaseolina was also provided by Dr. Chris Little. The pathogen was then subcultured into several fresh potato dextrose agar plates into which sterile toothpicks were inserted. The culture was incubated at 30° for 2 wk and the infested toothpicks were directly used for inoculation. Again, on d 14 after flowering, the remaining three blue-tagged plants in each plot were inoculated by inserting infested toothpicks into the basal stalks (approximately 10 cm above soil surface) through holes made with a sterilized needle  matrix as unstructured, thereby allowing estimation of unique genetic variance within each environment and a unique genetic correlation between each pair of environments. The same models used to compute repeatability for all stalk rot response parameters were used to estimate repeatability for days to flowering and plant height. Genetic correlations among stalk rot traits were estimated from least square means obtained from the combined analysis using a multivariate model in ASReml version 3  was observed between k = 4 and 5. The optimal k was determined based on the likelihood plot of models and stability of grouping patterns across 20 runs. Subsequently, we tested clusters 4 and 5 using an initial burn-in of 150,000, after which data were collected for an additional 350,000 steps with 20 replications for each cluster. Further, the membership coefficients for the most optimal number of clusters based on this analysis were permuted using the Greedy algorithm to match the various replicates for that value of k as closely as possible using CLUMPP software (Qk) were used for assigning lines to subpopulations. Lines with the highest membership probability  were considered to result from admixture and, hence, classified as “mixed.” Kinship (K) was estimated using the software SPAGeDi 1.4  that increase computational speed and statistical power were implemented by clustering individuals in groups in the Genome Association and Prediction Integrated Tool package (GAPIT) (2R statistic was used to assess the amount of phenotypic variation explained by the model. The procedure by The least square means of genotypes for the various disease parameters were used as the input phenotype. The genome-wide association study (GWAS) was conducted on the phenotype and 79,132 genome-wide SNP markers data using univariate unified mixed linear model ( (GAPIT) . An R2 sIn addition to the unified mixed linear model, a multi-locus mixed model (MLMM) . We thenPopulation structure analysis sorted the genotypes into four subpopulations that presumably reflected racial category of the accessions. Genotypes with mixed ancestry were dropped from the analysis. The frequency of alleles associated with low stalk rot infection was estimated. Based on the results of the GWAS analyses, the frequencies of alleles that reduced disease severity at significant SNPs were estimated within each subpopulation using the FREQ package in R software version 3.0.2 . At eachF. thapsinum and M. phaseolina was assessed in a sorghum association panel consisting of 300 genotypes. Mean disease rating among the genotypes was markedly variable in all test environments for both pathogen species. For F. thapsinum, mean disease score for TLL ranged from 13.3 to 346.7 mm at Manhattan and from 16.7 to 143.4 mm at Ottawa with location means of 40.9 and 55.6 mm, respectively  was significantly correlated with MLL and relative major lesion length (RMLL), but not with TLL. However, RMLL was significantly correlated with TLL as well as MLL for both pathogens. Moreover, TLL and RTLL in F. thapsinum were significantly and positively correlated with MLL in M. phaseolina, but not with all other disease traits. RMLL was also not correlated with any disease trait in M. phaseolina. However, RMLL in F. thapsinum was significantly and positively correlated with all disease parameters in M. phaseolina, except RMLL, for that pathogen. Correlations between plant height and stalk rot traits were contradicting. Plant height was significantly correlated with TLL and MLL both in M. phaseolina–based and F. thapsinum–based scores but not with RTLL and MTLL for both pathogens. Correlation between days to flowering and disease traits was negative and low or not significant for both pathogens, but there was a strong positive correlation between days to flowering and plant height.Genetic correlations were estimated between all disease-related parameters, plant height, and days to flowering . MLL andP ≥ 0.5. This subgrouping was largely based on botanical races of the genotypes. In the current study, a few of these genotypes were reassigned to the admixture group, perhaps due to a more stringent threshold for probability of membership, which is P ≥ 0.8. The majority of the bicolor group was reassigned to the admixed group. A majority of the genotypes that were reassigned from one of the population groups to the mixed group in the current analysis had probability of membership (P = 0.60–0.79) in their previously assigned group . This indicates that if the arbitrary threshold of P ≥ 0.5 were used, then the population subgrouping would have become similar to the previous grouping.Previous studies of these populations have assigned the genotypes to five subpopulations based on a probability of membership Figure S1, Figure S2, Figure S3, Figure S4, Figure S5, Figure S6, Figure S7, Figure S8, Figure S9, Figure S10, Figure S11, and Table S2). Given the complex nature of the inheritance of stalk rot resistance traits in sorghum, and also given that most of the significant SNPs in the unified mixed model were located within a 2-Mb region on chromosome 9 (Table S2), we used the MLMM of Figure S1, Figure S2, Figure S3, Figure S4, Figure S5, Figure S6, Figure S7, Figure S8, Figure S9, Figure S10, and Figure S11). Based on the MLMM, we identified 14 major effect loci associated with stalk rot resistance from the combined environment as well as individual location analyses. In the combined analysis, the optimal model detected two significant SNPs. One of the SNPs (S9_57816733) was significantly associated with MLL and TLL for F. thapsinum . Another SNP (S9_57222599) from the optimal model was also significant for both MLL and TLL for M. phaseolina . These SNPs had the strongest significant association signals in the unified mixed model (Table S2). The two SNPs individually accounted for 11% to 16% of the total phenotypic variation explained by the traits and the two SNPs are in linkage equilibrium (r2 = 0.45) with each other .The genetic basis of variation for stalk rot resistance in sorghum was examined using a unified mixed linear model that controls for population structure and familial relatedness. Three SNPs were significant for at least one trait at a genome-wide Bonferroni-corrected threshold that was estimated based on an effective number of independent tests . This SNP accounted for approximately 14% of the phenotypic variation observed for each of these traits (Table S2). Two additional SNPs associated with MLL in F. thapsinum (S2_64623580 and S7_55702883) were identified with the forward-backward stepwise approach of MLMM . The two SNPs accounted for 24% of the total phenotypic variation for the trait . The four SNPs accounted for 33% of the total phenotypic variation for RMLL , which together accounted for 34% of the total phenotypic variation observed for the trait. Similarly, one additional SNP (S8_39230116) was associated with TLL in M. phaseoina, which also accounted for 13% of the total phenotypic variation . Clearly, stalk rot resistance is subject to genotype × environment interactions, which might be related to the severity of the stress. Such effects will be reflected in the QTL × environment interaction leading to SNPs being significant at one environment but not at the other.Because of the significant G × E observed in the overall combined analysis, we also conducted an independent association study for the Manhattan (two seasons combined) and Ottawa locations to capture location-specific loci associated with pathogen response. Analysis of combined data from the Manhattan location identified one additional significant SNP (S9_57272115) associated with TLL in both e traits . This SNhe trait . Using tfor RMLL . For the2) were used to test whether the significant SNPs were in strong LD with each other . The presence of very strong LD would raise concerns about a high rate of false positives present in our results, whereas complete absence of LD between the significant SNPs would provide evidence of complete independence between QTL. We observed significant LD only between SNPs S9_57222599 and S9_57272115 (r2= 0.52), both located on chromosome 9 at 57222599 and 57272115 Mb, respectively. For the rest of the SNPs, we did not observe signs of strong inter-QTL LD resulting from population substructure and admixture within the panel even for closely linked significant SNPs observed in the combined analysis such as S9_57222599 and S9_57816733 both mapping on chromosome 9 (r2 = 0.20). With an average r2 value of 0.06 for all 91 possible combinations between the 14 significantly associated SNPs, most observed r2 values were <0.30. Only four, which were among the six possible combinations between SNPs S9_57222599, S9_57231207, S9_57272115, and S9_57816733, were greater than 0.3 and none exceeded 0.5 .Classic LD parameters (D′ and rP = 3.01 × 10−4). The second SNP, S9_57222599, on the same chromosome is represented more or less equally across all subpopulations.Allele frequencies at the 14 SNPs significantly associated with stalk rot resistance across the four major sorghum subpopulations were estimated . In the P = 2.02 × 10−16) and the one at SNP S7_58532122 in the durra subpopulation (P = 0.0013). Also, the frequency of the allele associated with resistance at the SNP located on chromosome 2 (S2_64623580) from the Manhattan combined data were again largely represented in the cadutaum and durra subpopulations (P = 1.41 × 10−7). For the other SNPs on chromosome 2 (S2_60129082), the allele reducing disease severity was significantly (P = 4 ×10−6) over-represented in the guinea and kafir subpopulations compared to the caudatum and durra. Another allele at SNP S2_27851064 on the same chromosome was significantly (P = 2 × 10−16) over-represented in the durra and guinea subpopulations and was much less in caudatum and kafir groups.The frequency of allele at SNP S9_57272115 associated with reduced disease severity in the combined analysis for Manhattan location was high in all subpopulations. The resistance allele in the second significant SNP (S9_57231207) was represented only in the caudatum and guinea subpopulations. On chromosome 7, the allele associated with low disease severity at SNP S7_55702883 was significantly over-represented among the caudatum subpopulation (P = 5.14 × 10−8) over-represented in the durra subpopulation. Also on chromosome 8, the allele at SNP S8_51279094 reducing disease severity was significantly (P = 5.14 × 10−8) over-represented in the durra and guinea, whereas the SNP on chromosome 9 (S9_57231207) was present and significantly (P = 14 × 10−8) over-represented only in the caudatum and guinea subpopulations  with plant height. Although the number of nodes crossed was often used to account for this, genotypes with variable internode length always appear different in their reaction to stalk rot, even if same number of nodes were crossed. Here, we introduced the parameter “relative lesion length,” which is the ratio of length of infection (mm) to plant height (cm) to circumvent this problem. However, the repeatability of these derived traits (RTLL and RMLL) for both M. phaseolina and F. thapsinum on a genotype mean basis was lower than the corresponding disease severity measures (TLL and MLL) for both pathogens. The repeatability estimate was obtained by modeling each genotype as a random sample from the reference population, modeled by a genotypic variance–covariance structure equal to the genotypic variance multiplied by an identity matrix. Thus, the possible reasons for low repeatability for the derived traits may be due to the lower variation for these traits in the population have been accounted for by plant height. Moreover, environmental stresses that increase disease severity such as drought and high-temperature stress tend to reduce plant height, resulting in inconsistent relative lesion length across environments and reducing repeatability.Taking these together, the present study showed marked variability for response to both Fusarium stalk rot, both diseases are triggered by high temperature and prolonged drought conditions during crop maturation  and plant height  and F. thapsinum , both located on chromosome 9. But none of these SNPs was significant when data were analyzed separately for each individual location. However, a different set of SNPs located on the same chromosome, S9_57272115 (SNP3) and S9_57231207 (SNP4) for Manhattan data and S9_56646280 (SNP5) for Ottawa data, became significant for both TLL and MLL for both pathogen species. Although the two SNPs that were significant in the combined analysis were not significant in individual location data, these five SNPs were within a 1-Mb region on chromosome 9. There appeared to be a relatively high LD between SNP1 and SNP2 (r2 = 0.52) and between SNP2 and SNP3 (r2 = 0.45). Thus, it is possible that many of these SNPs are associated with the same underlying causal variation. However, SNP4 and SNP5 are in low LD with the other SNPs, suggesting that they are distinct from the causal polymorphisms with which the other SNPs are associated.Association mapping of the SNP markers on the disease phenotype data pooled across all environments identified two SNPs significantly associated with low TLL and MLL in Analysis of these SNPs in relation to previous published results indicates that they co-localize with the previously described plant height locus dw1/SbHt9.1 (57272115 Mb)  in the combined data were linked to the Sb09g029260 gene that belongs to the chalcone and stilbene synthase protein family. Chalcone synthase is a key enzyme in the flavonoid/isoflavonoid biosynthesis pathway. Besides being part of the plant developmental program, chalcone synthase gene expression is induced in plants under stress conditions such as UV light and bacterial or fungal infection. Chalcone synthase expression causes accumulation of flavonoid and isoflavonoid phytoalexins and is involved in the salicylic acid defense pathway (The SNP identified on chromosome 9 from the Manhattan combined data was associated with another gene (Sobic.009G233100.1) that belongs to the AP2 transcription factor family found only in plants. This gene has been reported to code for proteins involved in regulation of disease resistance pathways . SimilarSignificant SNPs identified on chromosome 8 (S8_39230116) and chromosome 9 (S9_5664280) from the Ottawa data were located within genes of unknown function, (sobic.008G098200.1) and 9 (Sb09g027770), respectively. The other SNP on chromosome 3 (S3_8738404) is located downstream of gene (Sb03g008300) and codes for a BED zinc finger protein domain that is a major component of R proteins . InfectiFusarium stalk rot phenotype data in the global sorghum association panel and integrated those to discover and map loci associated with resistance to both diseases. We identified 14 loci, some of which were associated with a set of candidate genes, that appear to be involved in a set of functions controlling the plant defense response. This research serves as a basis for the identification of stalk rot resistance or defense-associated genes that advances our understanding of the complicated molecular mechanisms that regulate the plant response to M. phaseolina and F. thapsinum.In conclusion, progress in the understanding of the genetic basis of stalk rot resistance in sorghum is complicated by multiple factors. The difficulty of reproducing consistent field environments and the low-throughput phenotyping protocols currently used are among the limitations to breeding for stalk rot resistance. The current study is the first effort to apply GWAS to understand the genetic mechanisms underlying stalk rot disease resistance in sorghum. We generated 70,000 SNP marker data and robust charcoal rot and


Gossypium L.) fibers constitute a classic model system to investigate cell initiation and elongation. In this study, we used a high-throughput transcriptome sequencing technology to identify fiber-initiation-related single nucleotide polymorphism (SNP) markers and differentially expressed genes (DEGs) between the wild-type (WT) Upland cotton (G. hirsutum) Xuzhou 142 and its natural fuzzless-lintless mutant Xuzhou 142 fl. Approximately 700 million high-quality cDNA reads representing over 58 Gb of sequences were obtained, resulting in the identification of 28,610 SNPs—of which 17,479 were novel—from 13,960 expressed genes. Of these SNPs, 50% of SNPs in fl were identical to those of G. barbadense, which suggests the likely origin of the fl mutant from an interspecific hybridization between Xuzhou 142 and an unknown G. barbadense genotype. Of all detected SNPs, 15,555, 12,750, and 305 were classified as non-synonymous, synonymous, and pre-terminated ones, respectively. Moreover, 1,352 insertion/deletion polymorphisms (InDels) were also detected. A total of 865 DEGs were identified between the WT and fl in ovules at −3 and 0 days post-anthesis, with 302 candidate SNPs selected from these DEGs for validation by a high-resolution melting analysis and Sanger sequencing in seven cotton genotypes. The number of genotypic pairwise polymorphisms varied from 43 to 302, indicating that the identified SNPs are reliable. These SNPs should serve as good resources for breeding and genetic studies in cotton.As the longest known single-celled trichomes, cotton ( Gossypium hirsutum L., is native to Mexico, the West Indies, and northern South America. This species, which is grown in more than 80 countries, supplies almost 95% of the natural fiber, the raw material for different textiles [Upland cotton, textiles . Cotton textiles . Becausetextiles –5. In adtextiles –8. Cottofl mutant, which possesses no fiber , was originally discovered in its wild type (WT), the cultivar Xuzhou 142 with lint and fuzz fibers [fl mutant phenotype is controlled by two pairs of recessive genes (li3li3n2n2), but the location of the fl gene locus is still unknown [fl arose as a natural isolate from Xuzhou 142, its morphological characteristics have cast doubt on the isogenic nature of the two genotypes. Although a detailed genetic comparison of the two genotypes is needed, such an analysis is hindered by the nature of the complex cotton genome. Because the tetraploid genome of cotton is relatively large—approximately 2.5 Gb [Genetic mapping of major fiber mutants in cotton is an important approach for isolation of fiber-related genes and to provide clues to their functions. An Upland cotton fuzzless-lintless Xuzhou 142 z fibers . The two unknown . Althougy 2.5 Gb  and the y 2.5 Gb .Gossypium genome have been conducted. For example, An et al. identified gene family-targeted SNP markers [G. hirsutum cultivar, Acala Maxxa, and the wild landrace TX2094, which encompass the breadth of G. hirsutum diversity [G. hirsutum and inter-specific populations of G. hirsutum × G. barbadense [G. hirsutum. Insertion/deletions (InDels) cause similar levels of variation as SNPs, but with great diversity in size. Moreover, InDels can be distinguished easily and also become popular as genetic markers for mapping and other genetic research in crops.To date, many types of molecular markers, such as amplified fragment length polymorphisms, random amplified polymorphic DNAs, restriction fragment length polymorphisms, restriction-site-associated DNA markers, simple sequence repeats, and single nucleotide polymorphisms (SNPs), have been developed for applications to cotton research . To cons markers , while L markers . Deynze  markers . Byers eiversity . In addirbadense . To datefl mutant. In this study, we therefore applied next-generation RNA sequencing (RNA-seq) for the development of fiber initiation-related SNP markers based on an analysis of differentially expressed genes (DEGs) between the WT and the fl mutant. The gene markers identified in this study represent a valuable resource for genetic diversity assessment and mapping of agronomic traits leading to the isolation of the lintless and fuzzless genes in the fl mutant.The application of next-generation sequencing, including Roche 454 and Illumina Genome Analyzer systems, can accelerate the identification of new markers and genes and has a great potential to determine the intricacies of cotton fiber differentiation and initiation. Even though the 454 pyrosequencing technology has been applied to cotton genomics , the IllG. hirsutum ‘Xuzhou 142’ and its fl mutant line were grown at the Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, Henan Province, China. Three replications of the two genotypes were arranged side by side, with 10 plants per replication per genotype. Ovules from flower buds at −3 DPA and flowers at 0 DPA were collected, immediately dissected, frozen in liquid nitrogen, and stored at −80°C until use. Along with Xuzhou 142 and fl, five additional Upland cotton genotypes were used for SNP validation: Upland genetic standard TM-1, backcross inbred line (BIL)-7, BIL-13, Zhong-58, and Zhong-58 mutant. The BIL-13 with longer fiber length and BIL-7 were derived from Giza 75 (G. barbadense) and SG 747 (G. hirsutum) through two generations of backcrossing (BC2) with SG 747 as the recurrent parent, followed by self-pollination for four generations. Zhong-58 was a commercial cultivar while Zhong-58 virescent mutant was derived from cotton cultivar Zhong-58, showing yellow leaves at the seeding stage and longer fibers than its wild type.Upland cotton Total RNA was isolated using an RNAprep Pure Plant kit  according to the manufacturer’s instructions. After digestion of residual DNA with DNase I , RNA quality and quantity were measured on a Nanodrop 2000 instrument  and an Agilent 2100 Bioanalyzer . Samples with 260/280 ratios of 1.9 to 2.1 and RNA integrity numbers ≥ 8 were used in subsequent analyses.Poly(A) mRNA was isolated using oligo(dT) beads and then sheared into short fragments using a fragmentation buffer . The shoThe RNA-seq data used for this analysis have been submitted to the National Center for Biotechnology Information-Sequence Read Archive database (SRA) and assigned the identifier SRP056184.After adaptor trimming and removal of low-quality reads, RNA-seq reads were stringently aligned against the Upland cotton (AADD) TM-1 genome using the Burrows-Wheeler Alignment tool , 24. SNPGene expression levels were measured by RNA-seq quantification analysis using the Noiseq package . The fol−5, was used to obtain DEG Gene Ontology (GO) annotations [The Blast2GO program, with a cutoff value of 1 × 10otations . This anotations .fl, and the other five Upland cotton genotypes as described by Zhang and Stewart [fl, we amplified 302 SNPs to generate 200–500-bp fragments (2 (25 mM), 2 μL DNA (25 ng μL−1), and 5.2 μL deionized water. A touchdown PCR was initiated with melting for 10 min at 95°C, followed by 42 cycles of 95°C for 10 s, a touchdown cycling step of annealing from 65 to 56°C for 15 s, and 72°C for 20 s. After amplification, the PCR products were heated from 65 to 95°C and data was acquired at a rate of 25 scans per °C. To further validate the identified SNPs by Sanger sequencing, five SNPs were randomly selected from those successfully genotyped by HRM. Their associated PCR products were cloned into a pMD18-T vector  and transformed into Escherichia coli (DH5a). Subsequently, 6–10 clones were sequenced using the Sanger sequencing method.To validate SNP markers, genomic DNA was extracted from the WT,  Stewart . Using pragments . The PCRfl  (The average sequencing depth of the WT and se SNPs) . Transitse SNPs) . The prose SNPs) .G. barbadense genome, and 58.8% of these  showed polymorphisms from Upland cotton. Moreover, 50% of the SNPs from fl were the same as those in G. barbadense. These results clearly suggest that fl may have originated from an interspecific cross between Xuzhou 142 and an unknown G. barbadense genotype.A statistical summary of SNP read depths is given in fl were identified by comparing sequence reads between −3 DPA WT and −3 DPA fl and between 0 DPA WT and 0 DPA fl  are separated from heterozygous ones in an F2 segregating population is shown in The 302 validated SNP markers derived from the DEGs were also detected in five other cotton genotypes . The higfl and its isogenic WT, suggesting the non-isogenic nature of the two genotypes. Therefore, it should be recognized that most of the differentially expressed genes (DEGs) identified from our study were not related to the fuzzless and lintless genes and their expressions. Gossypium barbadense, also known as long-staple cotton, generally has no fuzz , a trait controlled by recessive n2n2. A comparative analysis revealed that more than half of the SNPs in fl were the same as those in G. barbadense, indicating that most of the SNPs identified in this study involved polymorphisms between Upland cotton and G. barbadense genomes. Consequently, the fl mutant may have arisen from interspecific hybridization between Xuzhou 142 and a G. barbadense genotype that contributed the recessive n2n2 gene to fl. Xuzhou 142 may carry another recessive gene li3li3 and its combination with n2n2 gave rise to the fuzzless and lintless mutant.Because of their advantages of genome-wide coverage, data quality, high genotyping efficiency and analytical simplicity , SNP marChHOX3, was found to control cotton fiber elongation in a previous survey of 281 cotton cultivars [GhHOX3 overexpression leads to longer fibers, whereas its silencing significantly reduces fiber length (> 80%). Those results indicate that SNP variation in GhHOX3 influences gibberellin signaling and thus affects fiber cell elongation.The top 20 genes with the highest number of SNPs are listed in ultivars . In thatGhMYB25, a good candidate gene for an association study of cotton fiber initiation, was highly expressed in 0-DPA ovules in our study. Moreover, the promoter of GhMYB25 activated the GUS reporter in ovule epidermal cells of 0-DPA and 10-DPA fibers in a previous study [GhMYB25 resulted in fewer fiber initials (10–20%) and inhibited fiber cell expansion as well as elongation. In contrast, over-expression of GhMYB25 led to more fiber initials (15–35%) [Transcription factors, which often form multi-protein complexes, bind with specific DNA sequences to increase or decrease gene transcription . To undeus study . Silenci(15–35%) .Triticum aestivum, another early auxin-responsive gene is induced by light, calcium, and epibrassinolide [in vitro can promote fiber initiation and increase total fiber units. In a previous investigation, the IAA biosynthetic gene iaaM driven by the petunia floral binding protein 7 gene promoter caused IAA to accumulate in cotton ovule epidermal cells from –2 to 10 DPA and also increased the number of lint fibers [Nonsense mutations introduce premature stop codons into genes and can lead to an incomplete, potentially harmful, and usually nonfunctional protein product. Consequently, nonsense mutations are associated with disease susceptibility in humans and seem to be disadvantageous . Some husinolide . Indole-sinolide . Applicat fibers .G. hirsutum cultivar and its fl mutant in this study should facilitate cloning of fiber-initiation-related genes and related functional genomic studies in cotton.Using the Illumina sequencing platform, we performed a deep transcriptome sequencing of cotton ovules during and prior to flowering and identified a number of potential SNP markers. The identified SNPs should serve as an important genetic resource for use in gene-based linkage mapping studies in cotton. In particular, the identification of SNPs from DEGs between a wild-type S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


The training set was validated with Fluidigm technology and showed 96% concordance with the genotype calls in the parents/F1 hybridizations. Application of the genotyping array was illustrated by constructing a 598.7 cM genetic map based on a ‘9930’ × ‘Gy14’ recombinant inbred line (RIL) population comprised of 11,156 SNPs. Marker collinearity between the genetic map and reference genomes of the two parents was estimated at R2 = 0.97. We also used the array-derived genetic map to investigate chromosomal rearrangements, regional recombination rate, and specific regions with segregation distortions. Finally, 82% of the linkage-map bins were polymorphic in other cucumber variants, suggesting that the array can be applied for genotyping in other lines. The genotyping array presented here, together with the genotype calls of the parents/F1 hybridizations as a training set, should be a powerful tool in future studies with high-throughput cucumber genotyping. An ultrahigh-density linkage map constructed by this genotyping array on RIL population may be invaluable for assembly improvement, and for mapping important cucumber QTLs.Genotyping arrays are tools for high-throughput genotyping, which is beneficial in constructing saturated genetic maps and therefore high-resolution mapping of complex traits. Since the report of the first cucumber genome draft, genetic maps have been constructed mainly based on simple-sequence repeats (SSRs) or on combinations of SSRs and sequence-related amplified polymorphism (SRAP). In this study, we developed the first cucumber genotyping array consisting of 32,864 single-nucleotide polymorphisms (SNPs). These markers cover the cucumber genome with a median interval of ~2 Kb and have expected genotype calls in parents/F Linkage maps and association studies are becoming increasingly valuable for plant genetic research –4 and asIn the last 5 years, the use of SNP arrays has been integrated into plant breeding studies and has leveraged marker-assisted selection . SNP arrCucumis sativus L., 2n = 2x = 14) genome was the first of the Cucurbitaceae family to be fully sequenced [Cucumis species [C. sativus var. hardwickii, PI 183967) has the highest marker density (995 SSR loci), but nearly one-third of the mapped loci are in clusters due to significant structural rearrangements between the cultivated and wild cucumber [2 mapping population [The cucumber  was extracted using the CTAB method  and purihttp://www.agilent.com); the hybridization temperature was modified to 55°C.Labeling of DNA pools and hybridization were performed at the Weizmann Institute’s DNA array unit following the Agilent CGH protocol for comparative genomic hybridization (http://bioinformatics.bc.edu/marthlab/wiki/index.php/Software) to the cucumber ‘9930’ draft genome version 1 (downloaded from http://www.icugi.org). Three criteria were used for selection among the 97,015 putative SNPs: genome coverage, SNP call, and SNP quality. The genome-wide determination was performed by running a 2-Kb window and selecting the best SNP within each window. The best SNP was defined as the SNP with the highest sequence read coverage of the minor allele and a base call quality of at least 25  or 57 . Four probes were designed per SNP. Two probes, as replicates, for each allele, with one replicate probe designed to detect the forward and the other the reverse DNA strand. The probes were Tm optimized as follows [Long reads yielded by 454-GS Flex Titanium sequencing of the 'Gy14' genome  and 100- follows :Tm = ΔHa et al.  and chosTo avoid paralogous SNPs, i.e., SNPs that are the result of repetitive sequences in the same genome, we used only reads that were mapped uniquely to the reference genome.http://www.fluidigm.com).SNP-type oligonucleotide assays were designed by Fluidigm based on our genotyping information. Samples were genotyped using the EP1 platform on 96 x 96 plates following Fluidigm procedures  quantile normalization was applied between arrays and per probe normalizationby subtracting the median of the probes over arrays, 2) spatial slide defects, e.g., scratches, bubbles, etc., were removed by the method described in Chi et al. , and 3) 1 progeny in six, five, and five replicates, respectively. On this array, the genotype calls are expected to be AA, AB, and BB for ‘9930’, F1, and ‘Gy14’ respectively. A K-mean clustering was applied to define three distinct clusters. Only probe sets that had three distinct clusters of the two parental lines and F1, or clusters that had one misclustered signal were used for genotype calling.A training set was prepared by hybridizing gDNA from ‘9930’, ‘Gy14’, and their FGenotype calling was performed by the Mahanalobis method . Each SN7:8 RIL population.Initially, we removed the noises that might affect the accuracy of the genetic map by: 1) leaving only one representative SNP for a set of SNPs whose genotype calls are exactly the same across all RILs, 2) filtering out SNPs with too many low-confidence calls, and 3) filtering out SNPs with observed heterozygous genotype frequencies that deviate from that expected in an F7:8 RIL population, the expected probabilities were 63/128, 2/128, and 63/128 for AA, AB, and BB, respectively. The frequency of genotype calls for each SNP was calculated and SNPs that deviated significantly from the expected distribution  were filtered out.For groups of SNPs with redundant genotype calls, the SNP with the most distinct signal clusters was selected to represent the group. Distances between all pairs of the three cluster types were calculated by Bhattacharyya distance . Each SNSNP discovery was performed on version 1 of the ‘9930’ genome reference, which was available at the time. When the genome reference was updated to version 2, we removed SNP positions that were inconsistent between the two versions from the map. As a result, 209 additional SNPs were filtered out for a total of 11,156 markers.http://alumni.cs.ucr.edu/~yonghui/mstmap.html) to construct a genetic map with the following parameters: genetic distance = halden, cut_off_p_value = 1e-10, no_map_dist = 15, no_map_size = 2, missing_threshold = 0.15, estimation_before_clustering = no, detect_bad_data = no, objective_function = ML. The outcome was a genetic map divided into bins where the start of each bin indicated the position of one or more SNP markers on the map. The order of the loci on the genetic map was used to refine unexpected genotype calls. As a result, an additional seven samples that had more than 10 ‘no call’ regions (described in the next section) were filtered out and a new linkage map of 117 RILs was constructed.Out of the initial 136 RIL samples that were hybridized on the array, two samples could not be normalized because of technical problems in the hybridization. In 10 samples, either the proportion of the heterozygous calls was higher than expected (0.2), or it was uninformative . Thus, data from 124 RILs were ultimately used in the linkage analysis. These data were submitted as input to ‘MSTMap’ program  vs. the physical distance (Mb) for each linkage group against the chromosome under comparison  Figs. PrThe 44,360 SNPs on the array were filtered by the criteria for redundant SNPs and for SNPs with too many low-confidence calls as described in the genotype call section. Genotype frequencies were calculated for each allele. The proportions of homozygous genotype calls, p(AA) and p(BB), were calculated per SNP. A spline non-linear regression of p(AA)–p(BB) was plotted against the genome position. A significant distortion from the allele segregation was calculated by chi-square test under the null hypothesis that the frequencies of p(AA) and p(BB) are equal and a Benjamini and Hochberg method  for multTwo properties should be taken into consideration when selecting a pool of SNPs for genotyping. Association studies ideally require that SNP markers cover the whole genome at high resolution, to decrease the chances of a recombination event between a SNP marker and the trait locus. The other property is the SNP marker's polymorphism in the segregating population under study. Commonly, enrichment with that type of SNP is achieved by selecting for SNPs that are polymorphic in a germplasm. Alternatively, by having a large pool of SNPs that cover the genome, one can expect to find a subset of SNPs that will be polymorphic and therefore informative in any association study.1 progeny can be used as a training set in the genotyping as they are expected to show all three types, i.e., AA, AB, and BB for each SNP. We implemented this approach by mapping both the 24,761,994 long reads (454 GS-FLX Technology) of ‘Gy14’ and the 88,086,537 short reads (Illumina HiSeq-2000) of ‘9930’ to the reference genome of the latter [Advanced algorithms for genotyping microarrays are based on machine learning techniques . These ae latter . SNPs weThe coverage of most (90%) of the SNPs on the array was in the range of 8–39 reads per locus with a median allele A-to-allele B coverage ratio of 1:2. 1 progeny. The hybridizations of the three samples which resulted in the expected genotype calls generated a training set for each SNP. Out of the 44,360 SNPs on the array, a subset of 32,846 SNP-probe sets formed expected genotype calls or a good training set (GTS). A GTS was defined if three distinct clusters of allele-A signals against allele-B signals were identified by K-mean algorithm  were compatible with ‘Gy14’ array calls. In addition, a synthetic F1 was tested by mixing the gDNA of the two parental accessions that resulted in a 100% match to the genuine F1. This result suggested that a mixture of DNA from the two parental lines might be an alternative solution to extracting the gDNA of F1.Agilent eArray technology enables generating a pool of probes and selecting a subset for future use. The criterion for probe-subset selection should rely on probes for which hybridization of the training set samples results in the expected genotype calls. Therefore, we performed hybridization with gDNA of the two parental lines (‘9930’ and ‘Gy14’) and their Flgorithm . Moreovelgorithm . Only 9.2 test, df = 1, p-values: 0.2–1; the range of p-values for all chromosomes for both genomes ‘9930’ and ‘Gy14’). Therefore, the GTS is a representative subset of the full set of SNPs on the array and using only the GTS subset should not significantly bias results.Narrowing the array to the GTS did not affect the proportion of SNPs in the CDS , as it wA genetic map based on 124 RILs was constructed using the GTS. When two or more SNPs had redundant genotype distribution along the RILs, only one was retained. SNPs with too many low-confidence genotype calls, SNPs with more heterozygous calls than expected, and uninformative SNPs were also removed. The genotype calls of the remaining SNPs were further improved by a stage of genotype call refinement based on the flanking vicinity in a haplotype block: after creating a genetic map, the genotype call was improved by modifying a call based on the flanking calls in a defined window . For exa2 = 0.97 average for all chromosomes) with both genomes for all chromosomes, indicating its accuracy. However, there was a deviation from collinearity of a long segment of markers on LG5. This deviation appeared in the comparison of the genetic map to the ‘9930’ genome but not to the ‘Gy14’ genome  and the lowest on chromosome 3 (2.4 cM/Mb). Although these maximum and minimum values did not exceed the 95% confidence intervals (1.7–4.7 cM/Mb), the recombination rate on chromosome 4 was close to the upper bound of the confidence interval for the whole-genome recombination rate.2 population [Recombination suppression has been previously reported on chromosomes 4, 5 and 7 in a segregating population of a cross between landrace cucumber accessions ‘PI 183967’ and ‘Gy14’ . These spulation . In the An ultradense genetic map provides the opportunity to investigate the regional rate of recombination and explore whether the recombination rate is uniformly distributed. By calculating the standard deviation of the local recombination rate along the chromosomes, we can estimate whether the recombination rate is uniform along each chromosome, or variable, i.e., subjected to a local effect. The local recombination rate was calculated as the log genetic distance (cM) to thegenomic distance (Mb) ratio along five markers within a running window . The sta2 population [7:8 of the RIL population. Three SDRs were observed, one on chromosome 1 (~70%) and two on chromosome 5   .To illustrate the applicability of this cucumber SNP array, we used it to genotype four inbred lines of cultivated cucumber: 'H19', 'TL', 'G421', and 'WI 2757'. Out of the 32,846 GTS SNPs, 93% were successfully genotyped (repeated in duplicate) in two out of the four accessions, and 77% in three accessions. Forty-seven percent of the SNPs on the linkage map were not polymorphic Click here for additional data file.S2 FigScatter plots of marker positions on the genetic map against their physical locations in the ‘Gy14’ genome (version 1.0). The order of the markers on the genetic map is highly collinear with their order in the ‘Gy14’ genome. The R2 values for chromosomes 1 through 7 are 0.99, 0.96, 0.99, 0.98, 0.96, 0.96, and 0.97, respectively.(TIF)Click here for additional data file.S3 FigScatter plots of marker positions on the genetic map against their positions in the ‘9930’ genome (version 2.0). The order of the markers on the genetic map is collinear with their order in the genome. The R2 values for chromosomes 1 through 7 are 0.99, 0.99, 0.99, 0.99, 0.82, 0.96, and 0.98, respectively.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.


Venezuelan equine encephalitis virus (VEEV) is a mosquito-borne alphavirus that has caused large outbreaks of severe illness in both horses and humans. New approaches are needed to rapidly infer the origin of a newly discovered VEEV strain, estimate its equine amplification and resultant epidemic potential, and predict human virulence phenotype. We performed whole genome single nucleotide polymorphism (SNP) analysis of all available VEE antigenic complex genomes, verified that a SNP-based phylogeny accurately captured the features of a phylogenetic tree based on multiple sequence alignment, and developed a high resolution genome-wide SNP microarray. We used the microarray to analyze a broad panel of VEEV isolates, found excellent concordance between array- and sequence-based SNP calls, genotyped unsequenced isolates, and placed them on a phylogeny with sequenced genomes. The microarray successfully genotyped VEEV directly from tissue samples of an infected mouse, bypassing the need for viral isolation, culture and genomic sequencing. Finally, we identified genomic variants associated with serotypes and host species, revealing a complex relationship between genotype and phenotype. VEE complex viruses are endemic to South and Central America, Mexico, and Florida [Venezuelan equine encephalitis (VEE) virus (VEEV) is a mosquito-borne alphavirus capable of causing large outbreaks of encephalitis in humans and horses. Major equine-amplified epidemics dating to the early 20 Florida . Althoug Florida .Enzootic VEE is also of concern due to its high burden of endemic human disease. For U.S. war fighters engaged in a conflict in Latin America, either direct exposure to the enzootic cycle in rural or suburban regions, as documented in Panama , 4, ColoThere are three major challenges related to VEE that we believe can be solved using new approaches: 1) rapidly estimating the origin of a newly discovered VEEV strain; 2) estimating its equine and/or human amplification, and thus epidemic potential; and 3) predicting the human virulence phenotype of a newly discovered VEEV strain. Phylogenetic relationships of a diverse collection of VEEV strains have proved useful for identification of the genetic features leading to epidemic spread to humans and livestock of this zoonotic pathogen , 12. HerWe performed whole genome SNP analysis of all available VEE antigenic complex genomes, verified that these SNPs accurately recapitulated the phylogeny from whole genome multiple sequence alignment (MSA), and developed a high-resolution genome-wide SNP microarray. We analyzed a diverse panel of 133 VEEV isolates on the microarray to validate array-based SNP calls with previously sequenced strains, and to characterize the SNPs in unsequenced isolates and place them on a phylogeny with sequenced genomes. We explored the relationship between genome variation and serotype, identified a number of variants non-randomly associated with these phenotypes, and examined the distribution of these variants across the VEEV genome.k-mers (oligos of length k) in the genome sequences. We define a SNP locus by a sequence context of length k centered on the polymorphic base, with (k-1)/2 conserved bases on either side. For this study, we performed SNP analysis with k = 13. Note that, under this definition of SNP loci, multiple loci (corresponding to different variations of the k-mer context) may overlap the same positions in a multiple sequence alignment; in this case, each of the multiple loci is only considered to be present in the genomes in which the (k-1) base context is conserved. This alignment-free SNP discovery is useful for viruses in which there may be highly divergent and poorly alignable regions among a large group of sequences, and where conserved regions only exist among small subgroups of sequences. The kSNP approach is free of the bias that otherwise results from the choice of a reference sequence, or from considering only a subset of regions of the genome that can be easily aligned, and can be implemented at scales to hundreds of genomes. We calculated SNP-based phylogenetic trees using parsimony, maximum likelihood (ML), or neighbor joining (NJ). For NJ, we used the number of SNP allele differences between pairs of target sequences as the distance metric. We mapped SNP alleles to branches of the trees using kSNP.We applied the kSNP software to find SNPs in the 144 VEE antigenic complex genomes available as of June, 2014 , 14. kSNSNPs from the E1, E2, E3, and capsid genes were extracted for separate analyses by identifying those SNPs that occurred within the specified gene regions . We consth position from the 5’ end of the probe (the end farthest from the array surface), probes were between 32 and 40 bases long, and lengths were chosen to equalize hybridization free energy (ΔG) to the extent possible within the allowable length range. We found that probes shorter than 32 bases had high false negative rates, and longer probes did not discriminate well between alleles. We found that ΔG was a better predictor of hybridization than the melting temperature Tm. Probe candidates with hybridization free energy below ΔGmin = -43 kcal/mol were shortened until either their free energy exceeded ΔGmin or they reached the minimum 32 bases. Probes were designed around the SNP on both the plus and minus strands, for all observed SNP alleles, and all surrounding sequence variants.We designed microarray probes for every SNP locus. Our probe design strategy maximized sensitivity and specificity based on extensive prior lab testing on a Roche NimbleGen microarray platform, where we demonstrated 99.52% SNP allele call rates and 99.86% accuracy . After tProbes for the plus and minus strands were not the reverse complements of one another because the SNP does not lie at the center of the probe. We included probes for all observed alleles on each strand, yielding at least four probes per SNP locus for biallelic SNPs. In addition, we captured any sequence variation outside of the conserved k-mer SNP context in multiple alternative probes for each allele, so that some biallelic loci had more than 4 probes. Finally, we trimmed probes from the 3’ end to remove any N’s or other ambiguous bases, and omitted them altogether if doing so resulted in a probe shorter than 32 bases. When a probe was a subsequence of any other, only the shorter of the two was kept. SNP microarrays were fabricated using the 12-plex 135K Roche NimbleGen array format with 89% of the probes tiled in duplicate.The VEEV cDNA samples were fluorescently labeled and hybridized to VEEV SNP arrays as described previously . BrieflyBased on temporal and geographic range, outbreak associations and prior sequences generated at UTMB, we identified, propagated, and isolated RNA for microarray experiments from 134 of the most representative strains. To enable comparison of array- and sequencing-based genotyping methods, we included 81 isolates that had previously been sequenced in the set of strains tested on the array. Three of the previously sequenced isolates and one unsequenced isolate were run on duplicate arrays, for a total of 138 arrays. The serotype, passage history, year and location of collection, and host of each strain are listed in 4 PFU in a 20 μL volume. Three biological replicate mice were infected and sampled. Brains were harvested two days later and homogenized in a 1:10 w/v solution. The suspension was clarified by centrifugation and stabilized in Trizol (Life Technologies). RNA was extracted and purified using the Direct-zol RNA MiniPrep kit  according to the manufacturer’s instructions. Whole cDNA was synthesized, fluorescently labeled, and hybridized to the SNP microarray as described above.To test the array’s ability to genotype viruses directly from tissue samples, six day-old CD-1 mice were infected with VEEV vaccine strain TC-83  via the Data from microarray experiments is available at the Gene Expression Omnibus (GEO) repository under accession GSE79530.We used our previously developed analysis software to call alleles at each locus for each sample analyzed on SNP microarrays. The software fits a linear model of strand and allele effects to the log intensity data from all probes for the locus, and calls the allele as the one with the largest coefficient in the fitted model. Separating the strand and allele effects is necessary in order to compensate for the differing hybridization efficiencies often seen between forward and reverse strand probes.Because our definition of a SNP locus requires conservation of the 6 bases on either side of the polymorphic base, array probes for one locus may hybridize to genomes in which a similar locus context is present. That is, loci that are considered to be different in the sequence analysis, but have 13-mer contexts that are identical except at one or two positions, may be difficult to distinguish by microarray probes. Therefore, our current array analysis software does not attempt to determine whether a locus is present or absent, and instead makes an allele call for every locus.For isolates that had genome sequences available, we computed the concordance rate between the allele calls from the array and the genome sequence, defined as the percentage of loci present in the genome for which the array calls agreed. We also computed the numbers of allele differences between each array sample and each genome, and determined whether the closest genome was in fact the genome sequence for that strain.https://github.com/stamatak/Parsimonator-1.0.2). We generated 100 trees using different random number seeds, and selected the most parsimonious  for downstream analyses.We used the genotype data from genomic sequences to create maximum parsimony phylogenetic trees, using Parsimonator  or “small” (rodents and mosquitos). For each phenotype (serotype and host type), we built multiple tree classifiers using a 10-fold cross-validation scheme, in which classifiers were trained with 90% of the isolates and tested with the remaining 10%. The amount of pruning in each decision tree classifier was determined by a complexity parameter; the “rpart” algorithm automatically determined an optimal complexity, defined as the smallest parameter value that yielded a cross-validation error rate within one standard deviation of the minimum error rate. We then built a final decision tree for each phenotype with the full set of genomes, using the optimal parameter to control the complexity of the tree. For each phenotype, we tested initial locus sets consisting of all polymorphic loci present in the TC-83 reference genome, as well as restricted sets containing only non-synonymous loci within the genes encoding structural proteins, or within the envelope glycoprotein genes.For each classifier, we computed an overall accuracy, defined as the percentage of phenotype predictions that were correct. We also computed performance metrics for each specific phenotype, treating the decision tree as a binary classifier; e.g. for classifying isolates as serotype IAB vs any other serotype. For each specific phenotype, we counted the true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN), and used them to compute the accuracy = (TP + TN) / (TP + FP + TN + FN), positive predictive value (PPV) = TP/(TP + FP), negative predictive value (NPV) = TN/(TN+FN), true positive rate TPR = TP/(TP+FN), and true negative rate TNR = TN/ (TN+FP).We also ranked loci according to their strength of association with serotype or host type according to Fisher’s exact test, as implemented in the R “fisher.test” function. We corrected p-values for multiple comparisons using the Benjamini-Hochberg method.To identify single nucleotide variations among VEEV strains, we applied the kSNP software to 144 VEE antigenic complex genomes. We identified 7,926 SNP loci among these strains. The numbers of SNPs identified in structural protein encoding regions are summarized in When we reran the kSNP analysis, using as an outgroup four strains of eastern equine encephalitis virus , the total number of SNP loci increased to 9,486.We then examined phylogenetic relationships among strains by building trees using different methods. First, we wished to determine which of the SNP-based tree construction methods performed best, by comparing the resulting trees to trees based on whole genome multiple sequence alignment (MSA). We found that the SNP tree built using maximum parsimony  was moreAlmost all VEEV strains could be uniquely identified by their genotypes according to variations across the identified SNP loci. Numbers at the interior nodes of the tree in To explore the relationship between the phylogenetic groupings of VEEV strains and their phenotypes, we examined the maximum parsimony tree shown in Similarly, we found that phylogenetic groupings were not strongly associated with particular hosts; the broad associations that did appear were likely artifacts of the different sampling strategies used for enzootic (serotype ID and IE) strains, which account for all samples from mosquitos and sentinel hamsters, and for epizootic (serotype IAB and IC) strains, which comprise most samples from equids and humans.Finally, when we examined the collection dates of samples found in each major clade, we found that many clades were remarkably persistent. For example, the serotype IAB epizootic strains (and associated type ID outlier) showed little genetic variation, even though they were collected over nearly 40 years (1938–1977) across a wide geographic area, from the USA through Guatemala and Trinidad down to Venezuela and Peru, likely the result of incompletely inactivated vaccines made from older strains initiating later outbreaks . LikewisBecause the host and serotype associated with a VEEV isolate are not completely predictable from its position in the phylogeny, we searched for SNP loci that were associated with these important phenotypes for which the association was not simply a product of ancestry. We applied the “rpart” recursive partitioning algorithm to identify variations that are associated with particular host types or serotypes. The resulting decision tree classifiers are diagrammed in Figs Depending on the true serotype of the isolate, serotype prediction accuracy ranged from 95.6% for ID to 98.5% for IAB and IE strains . SerotypThe SNP at position 9201 corresponds to residue 213 on the E2 protein; substituting G for C at this locus was shown previously to mediate a shift from serotype ID to IC . AlthougThe SNP at position 7764 lies within the capsid gene; an A or G at this position, corresponding to lysine or arginine at residue 68, is associated with serotypes IAB and IC respectively. The only strains not classified correctly by this SNP are the three serotype ID strains that are also misclassified by the SNP at 9201. The serotype data was obtained from previous studies, and it is possible that the serotypes were incorrectly determined.To assess whether other loci would perform equally well for predicting serotype, we performed mutual information clustering to identify equivalence groups of loci, such that knowing the allele at one locus in a group completely determines the alleles of the other loci. A total of 4126 loci were present in the TC-83 genome and were polymorphic across all VEEV genomes. The largest equivalence group comprised 666 loci, which were those that have one allele for the serotype IE branch of the phylogenetic tree (including the 3 serotype ID outliers) and a different allele for the IAB/IC/ID branch. The remaining equivalence groups ranged in size from 2 to 76 loci; 2124 loci are singletons. The loci at positions 9987, 9201 and 7764 used in the serotype classifier are all singletons, having distinct patterns of alleles across the full set of isolates. The SNP at 9201 is also a singleton with respect to the 71 isolates in the non-IE subtree of the decision tree. However, across the 24 isolates in the IAB/IC subtree, there were 14 non-synonymous loci, 7 of which were in structural protein genes, which have the same allele pattern as 7764. Any of these loci would perform equally well in distinguishing serotype IAB and IC isolates, once the likely ID and IE isolates have been excluded by testing the loci at 9987 and 9201. Therefore, it would be premature to identify any one of these loci as determining the serotype IAB vs IC phenotype.Host type prediction was less accurate than serotype prediction; 89.6% of strains were correctly predicted to have been collected from large mammals vs “small” hosts  . This maWe also built classifiers in which the predictors were restricted to non-synonymous loci within the genes encoding structural proteins, or further restricted to envelope protein genes. Serotype classifiers based on structural protein loci were more accurate than envelope glycoprotein-restricted classifiers (data not shown), but not as accurate as unrestricted classifiers. For host type prediction, the best overall classifier used envelope protein loci only, so restricting to smaller locus sets had no effect.We hypothesized that phylogenetic analyses of VEEV based on comparing single gene sequences, as was done in some earlier studies (2), would yield trees with lower resolution and differing topology than whole-genome MSA and SNP-based trees. To assess the impact of a single-gene approach, we compared the maximum parsimony tree based on all SNPs against trees generated using only the SNPs in each of the structural protein genes. We found that only 47% to 58% of the splits from the all-SNPs tree are present in any of the individual envelope gene trees . Since oTo compare the topologies of trees generated with whole-genome SNPs or MSA to single-gene trees, we generated tanglegrams. To address the question of whether microarrays provide a viable alternative to whole-genome sequencing for VEEV strain characterization, we developed a VEEV SNP array. The array included 70,760 probes covering all 7,926 loci discovered with kSNP. We hybridized cDNAs from 134 isolates to SNP arrays. Genome sequences were available for 81 of the isolates. We calculated overall concordance rates between the allele calls made by SNP microarray versus those called by whole genome sequences; these are summarized in A potential advantage of microarray analysis over DNA sequencing is its reduced need for viral isolation and culturing, allowing viruses to be characterized directly from a tissue sample. To test whether this was feasible, we isolated RNA from the brains of 3 replicate mice infected with VEEV strain TC-83, analyzed the RNA using the SNP microarray, and compared the array genotypes to our panel of 144 sequence-based genotypes. For all 3 replicates, the array genotypes were closest to those of the published sequence for the TC-83 strain, as shown in Tools for rapid genotyping of equine encephalitis virus strains and elucidating their phylogenetic relationships are critically important to understand why certain strains are likely to cause epizootic infection, and to forecast the incidence of potential epidemic events. The results above represent analyses of VEE complex strains derived from a wide range of hosts and geographic regions. The collected data indicate that our microarray and sequencing-based genotyping tools effectively distinguish VEEV strains and allow us to cluster those strains according to their derivation and phenotypic history.k-mer contexts defined by kSNP (data not shown). The MSA approach is usually not feasible for bacterial genomes, so that kSNP is typically a better option for bacterial genotype/phenotype association studies.Since the VEEV genome is small, whole genome multiple sequence alignment (MSA) of more than 140 sequences was feasible. Predicted genotype/phenotype associations were slightly more accurate when genotypes were based on variable positions in a whole genome alignment than when they were based on Relying on non-random associations between serotype and sequence variation, we were able to build decision trees to predict VEEV serotype. With 3 loci, prediction accuracy was 95.6% to 98.5%. However, strains that clustered phylogenetically with a different serotype were sometimes mislabeled by the decision trees. In addition, there were multiple loci that distinguished equally well between IAB and IC strains, after excluding isolates with a T at position 9987 (which are mostly serotype IE) or a C at position 9201 (which are mostly ID). These observations suggest that the actual amino acid variants that determine serotype may be any of a wide range of candidates, as suggested earlier , and thaCulex mosquitos, forest habitats) to the epizootic cycle . It reported that a single mutation in the E2 protein (T213 -> K or R), when engineered into a serotype ID enzootic strain, changed its serotype to IC and rendered it capable of causing enhanced viremia in horses, as well as possibly more efficiently infecting Aedes (Ochlerotatus) mosquitos implicated as vectors in equine-amplified epizootics. Our results, based on the larger set of VEEV genomes available now, suggest a more complex association between genotype and phenotype. We identified three serotype ID strains with the K213 E2 allele. There was no single locus that distinguished all of these ID strains from the IAB and IC strains nearest to them phylogenetically. This is further evidence that a variety of mutations can mediate the shift from ID to IAB or IC serotypes.A previous study  based onComparison of the phylogenetic tree predicted from whole genome SNPs was similar to that from whole genome multiple sequence alignment. Narrowing to single gene SNP trees showed that the E1 gene SNPs more closely represent the whole genome SNP tree than do the SNPs from the other envelope protein or capsid genes. This concurs with previous analyses based on sequence alignment rather than SNPs, which also showed that the E1 gene captures the same high level relationships as the whole genome alignment but does not provide the same resolution . Howeverk-mer based approach to SNP discovery has limitations relative to full sequence alignment, particularly for highly variable RNA viruses. However, our comparison of data derived from multiple sequence alignments versus SNP analysis revealed that the resultant trees were very similar and reliably identified comparable splits. These observed similarities are important in that they support the use of our unique SNP array as an effective detection and genotyping tool without available whole genome sequence data. The SNP array results can be obtained within 24 hours as compared to 48–72 hours by whole genome sequencing. The cost of running a sample on SNP array is roughly 10 times less than whole genome sequencing. We have shown here that data obtained from SNP arrays are capable of reliably clustering strains in accordance with their respective whole genome sequence data. Array data provide sufficient accuracy in phylogenetic classification to correctly cluster isolates by clade and to identify the closest neighbors that have been sequenced or hybridized to the array. This technology would be particularly useful for rapidly evaluating a novel strain from an epizootic outbreak event. Further evaluation of the SNP array, using unknown or unsequenced VEEV strains, could provide additional validity and value of this technology in detection and genotyping of outbreak strains.A S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableRows in bold text indicate replicate arrays for the same isolates.(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


Calving difficulty and perinatal mortality are prevalent in modern-day cattle production systems. It is well-established that there is a genetic component to both traits, yet little is known about their underlying genomic architecture, particularly in beef breeds. Therefore, we performed a genome-wide association study using high-density genotypes to elucidate the genomic architecture of these traits and to identify regions of the bovine genome associated with them.SIGLEC12, CTU1, and ZNF615). Nevertheless, only CTU1 contained a missense variant with a putative impact on direct calving difficulty based on SIFT (0.06) and Polyphen (0.95) scores. Using imputed sequence data, we refined a genomic region on chromosome 4 associated with maternal calving difficulty in the Holstein-Friesian population and found the strongest association with an intronic variant in the PCLO gene. A meta-analysis was performed across the three breeds for each calving performance trait to identify common variants associated with these traits in the three breeds. Our results suggest that a portion of the genetic variation in calving performance is common to all three breeds.Genomic regions associated with calving difficulty  and perinatal mortality were detected using two statistical approaches: (1) single-SNP (single nucleotide polymorphism) regression and (2) a Bayesian approach. Data included high-density genotypes on 770 Holstein-Friesian, 927 Charolais and 963 Limousin bulls. Several novel or previously identified genomic regions were detected but associations differed by breed. For example, two genomic associations, one each on chromosomes 18 and 2 explained 2.49 % and 3.13 % of the genetic variance in direct calving difficulty in the Holstein-Friesian and Charolais populations, respectively. Imputed Holstein-Friesian sequence data was used to refine the genomic regions responsible for significant associations. Several candidate genes on chromosome 18 were identified and four highly significant missense variants were detected within three of these genes  contains supplementary material, which is available to authorized users. Dystocia, more commonly known as calving difficulty, is defined as a prolonged or difficult parturition, often with assistance required during delivery. The adverse effects of calving difficulty have been well documented including an increased risk of dam and calf mortality, as well as a reduction in dam and calf performance, which have a cumulative impact on herd profit –3. The fCalving difficulty and perinatal mortality are complex quantitative traits, which are believed, like all traits of this nature, to be influenced by many genomic polymorphisms of individually small effect . AccuracThe objective of this study was to perform a GWAS based on genotypes obtained with the Illumina bovine high-density BeadChip that comprises 777 962 SNPs, to identify regions of the genome associated with three calving performance traits: (i) direct calving difficulty, (ii) maternal calving difficulty and (iii) direct perinatal mortality, in three cattle breeds  and to refine the detected genomic regions using (imputed) whole-genome sequence data.−8) from Hardy-Weinberg equilibrium within a breed were removed. After SNP editing, 605 718, 602 372 and 600 980 autosomal SNPs remained for the analysis of the Holstein-Friesian, Charolais and Limousin data, respectively. Sporadic missing genotypes were imputed using Beagle [Illumina bovine high-density (HD) SNP genotypes were available for 2660 dairy and beef bulls, which included 770 Holstein-Friesian, 927 Charolais, and 963 Limousin animals. All animals had a genotype call rate greater than 95 %. Genotypes that were available on both the sire and son(s) were used to confirm parentage. The presence of opposing homozygous genotypes for the 1929 sire-son pairs was examined to determine Mendelian inconsistencies. Among the 777 962 high-density SNPs, 3554 autosomal SNPs with a Mendelian error rate greater than 2 % were discarded. The genotypes of sire-son pairs were set to missing for the remaining SNPs for which sporadic Mendelian inconsistencies were found. In addition, 1574 SNPs with a GenTrain score  less than 0.55, 40 934 non-autosomal SNPs and duplicate SNPs, and 17 273 SNPs with a call rate less than 95 % were also discarded. SNPs with a minor allele frequency (MAF) less than 0.02 within each breed were discarded for that breed but retained in the other breeds. Finally, SNPs that deviated  and their associated reliabilities for all 2660 dairy and beef bulls were obtained from the Irish Cattle Breeding Federation database from the December 2013 national genetic evaluation. Direct and maternal PTA for calving difficulty and direct PTA for perinatal mortality were obtained from this evaluation; there is currently no maternal component estimated for perinatal mortality in the Irish national genetic evaluations. Heritability estimates used in the national genetic evaluations were equal to 9 % for direct calving difficulty, 2 % for maternal calving difficulty, and 2 % for perinatal mortality. Irish genetic evaluations across dairy and beef breeds use multi-trait and multi-breed models. Ireland has a long history of crossbreeding including a substantial use of purebred beef sires on dairy cows (36 % of dairy cows in 2013) and also of purebred beef sires on crossbred beef cows.In Ireland, calving difficulty is subjectively scored by producers on a linear scale of 1 to 4, where 1 = no calving assistance; 2 = slight assistance ; 3 = considerable assistance ; 4 = veterinary assistance (including caesarean). Perinatal mortality is recorded as a binary variable by producers, which indicates whether the calf died within a 24-h period after birth. It is a legal requirement in Ireland to record all animal deaths, including stillbirths. Phenotypic data were edited as described in detail by Purfield et al. . PTA forAssociation analyses were performed within each breed separately using two methods: single-SNP regression and a Bayesian approach.Single-SNP regression (SSR) analysis was undertaken in Wombat  using a h2 is the heritability of the trait, ri2 is the adjusted reliability of the animal and c is the proportion of genetic variation that is not explained by SNPs and set at 0.9 for the SSR analyses. After testing various values of c  that all had a minimal impact on the results, a value of 0.9 was chosen because it allowed defining a weight for each SNP in order to attribute up to 10 % of the genetic variance to calving performance traits. Boddihireddy et al. [c values of 0.1, 0.5 and 0.7. Test statistics for all SNPs were obtained. Multiple-testing correction was applied using the Bonferroni correction method and p-values were transformed into their corresponding q-values assuming a false discovery rate of 5 % [where y et al.  also doce of 5 % .Within-breed SSR p-values for each SNP were combined, within trait, using the weighted Z-score method. The combined weighted Z-score was calculated as:wi was the square root of the sample size of breed i and ±1 for the sign of the direction of the effect for each breed i) where Φ is the standard normal cumulative distribution function and pi is the p-value for that SNP in breed i. All SNP Z-statistics per breed (Zi) were corrected for multiple-testing prior to combining the values together. This multiple-testing correction involved dividing each Z-statistic per breed by a lambda factor, computed as the median of all Z-statistics in breed i divided by the expected median of the Z-statistics under the null hypothesis of no association in breed i. Weighted Z-scores were then transformed into their corresponding p-values.where the weight The Bayesian approach was carried out by fitting all SNPs simultaneously in a two-step Bayes procedure based on two different Bayesian models: (1) BayesC  followedc (proportion of genetic variation that is not explained by SNPs) was assumed to be 0.1. Since a Bayesian method simultaneously fits all SNPs into the model, a value of 0.1 was chosen so that the genotypes account for 90 % of the genetic variation. All Bayesian algorithms were applied using GenSel, a web-based program (https://pods.iplantcollaborative.org/wiki/display/DEapps/GenSel) developed by Fernando and Garrick [BayesB used the posterior genetic and residual variance estimates from BayesC and the calculated π value. The chain length for all Bayesian analyses was 50 000 iterations, with the first 10 000 iterations discarded as burn-in. The dependent variable was weighted as described previously but the value of  Garrick  within eBayes factors (BF) were used to quantify the strength of the posterior QTL probabilities for all loci  using thPr (H1) was calculated as 1-π  and the proportion of post-burn-in iterations that included the SNP in the model was used as evidence for an association. Bayes factors indicate the strength of an association based on a range of values: a BF greater than 3.1 indicates ‘substantial evidence’ that the SNP is associated with a QTL, a BF greater than 10.1 indicates ‘strong evidence’ and greater than 30.1 ‘very strong evidence’ [vidence’ . The prohttp://ensembl.org) and NCBI map viewer (http://www.ncbi.nlm.nih.gov/mapview/) on the UMD 3.1 genome build was then completed by focusing on linkage-disequilibrium (LD) blocks that contained the SNPs of interest. QTL regions were compared to previously reported QTL in CattleQTLdb  for calving difficulty or perinatal mortality as well as potentially correlated traits such as birth weight and animal size.Haplotypes were identified within a 100-kb region up and downstream of each SNP for which a BF greater than 200 was detected in the three breeds for either direct calving difficulty or maternal calving difficulty, or greater than 60 for perinatal mortality. These thresholds were chosen to restrict the haplotype analysis to the strongest associations identified for each trait. Gene search using Ensembl (http://www.1000bullgenomes.com/) (run 4) were available for 311 Holstein-Friesian bulls, of which 64 also had high-density genotypes. The 1000 bull genomes project identified 35.2 million SNPs across the bovine genome. The average genome coverage was 11.0X. Sequence data for the Holstein-Friesian animals used in our GWAS analysis were imputed for significant regions of the genome that were identified to be associated with calving performance in the Holstein-Friesian population analysed here. Significant regions were chosen based on clear peaks of association identified in the high-density GWAS with –log10 p-values greater than 8  or else based on results from previous studies which suggested the presence of QTL that affect calving performance on the chromosomes with peaks (chromosomes 6 and 10) [2 value of 0.17).Whole-genome sequence data from the 1000 bull genomes project  (http:// and 10) , 26. Thehttp://www.ensembl.org/info/docs/tools/vep/index.html) was used to provide SIFT scores where possible and PolyPhen scores were computed in our study.Any potential missense variants that were identified within genomic regions of strong association were analysed by SIFT (sorting intolerant from tolerant)  and PolyDirect calving difficulty refers to the characteristics of the calf itself  and its impact on the birth process. Several SNP associations for direct calving difficulty were detected for each of the three breeds, although the exact location and direction of the effects of these associations differed between breeds. Irrespective of statistical significance, estimated allele effects had opposite signs at 283 042 SNPs for the Holstein-Friesian and Charolais populations, at 285 133 SNPs for Holstein-Friesian and Limousin populations and at 284 509 SNPs for the Charolais and Limousin populations.−4; q = 2.0 × 10−4; BF = 1684.26). SNP ARS-BFGL-NGS-109285 and an adjacent SNP, BovineHD180001676, that was 41 kb away, were cumulatively included in the Bayesian model in 94.96 % of the Gibbs chains and together they accounted for 2.49 % of the genetic variance in direct calving difficulty. Alleles of both SNPs were in complete LD. Only 0.84 % of the Holstein-Friesian animals were homozygous AA for SNP ARS-BFGL-NGS-109285 and these animals had, on average, a greater PTA for direct calving difficulty  than heterozygous  and homozygous animals CC .Twenty-three SNPs located on chromosomes 18 (10 SNPs) and 6 (13 SNPs) had a q-value less than 0.05 for direct calving difficulty in the Holstein-Friesian population. SNP ARS-BFGL-NGS-109285, located on chromosome 18, exhibited the strongest association with direct calving difficulty . The SNP that was most strongly associated with direct calving difficulty (p = 9.3 × 10−11) was located ~150 bp away from three micro-RNAs, i.e. bta-mir-99b, bta-let-7e and bta-mir-125a. Sixteen genes and gene products contained SNPs with a p-value less than 2.5 × 10−8 and four of these were classified as missense variants, which were distributed across three genes; two were detected in the SIGLEC12 mRNA, one in the CTU1 and one in the ZNF615 gene . All four variants were classified as ‘tolerated’ based on SIFT scores, although the missense variant in CTU1 was at the limit of this category (SIFT score = 0.06). PolyPhen scores indicated that the amino acid substitution alanine to valine in the CTU1 missense variant could be deleterious (PolyPhen score = 0.95). The remaining detected missense variants were classified as ‘benign’ substitutions by PolyPhen.Due to the moderate proportion of genetic variation in direct calving difficulty accounted for by the genomic region between 52.58 and 62.58 Mb on chromosome 18, this region was further investigated using imputed sequence data and found to include 85 869 SNPs as illustrated in Fig. −8) [See Additional file ENSBTAG00000004082 was strongly associated with direct calving difficulty (p = 7.02 × 10−6). Other possible candidate genes detected included the CORIN gene which encodes a serine protease, and the CLOCK gene which functions as a maternal mRNA that regulates events in the oocyte and pre-implantation embryo. Similarly, no significant missense variants were detected for chromosome 10 [See Additional file Analysis of whole-genome sequence data for genomic intervals that flanked significant SNPs on chromosomes 6 (67.02 Mb to 77.02 Mb) and 10 (96.72 Mb to 104.30 Mb), for direct calving difficulty was also undertaken [See Additional file −6; q = 4.65 × 10−6; BF = 3310.78). Eight SNPs within this region between 5.51 and 5.73 Mb on chromosome 2 remained significant after Bonferroni correction (p = 9.97 × 10−6 to 0.004). The SNP with the strongest Bayesian association was included in the model for 70.17 % of the Gibbs chains and accounted for 3.13 % of the genetic variation in direct calving difficulty in the Charolais breed. Only 0.9 % of the Charolais animals were homozygous AA for this SNP and a greater PTA for mean calving difficulty was observed for these animals  than for the heterozygous  and homozygous animals GG . This SNP was located within an intron of the TMEM194B gene surrounded by three other possible candidate genes . In the Limousin breed, a strong association with direct calving difficulty was also detected on chromosome 2 . Several growth factor genes and the myostatin gene reside within this genomic region. In the Limousin breed, additional Bayesian associations were also identified on chromosome 3 [See Additional file DCAF6 gene. Two strong Bayesian associations were also detected on chromosome 19 in the Charolais population [See Additional file PRKCA gene and accounted for 1.42 % of the genetic variation in direct calving difficulty.Both the SSR and Bayesian methods detected SNP associations on chromosome 2 for direct calving difficulty in the Charolais population   [See Additional file The strongest association for direct calving difficulty detected by the meta-analysis across the three breeds was a 1-Mb region between 5.6 and 6.6 Mb on chromosome 2 ; however, no candidate gene was identified in this region. The four strongest associations in the SSR analyses were detected within a 20-kb region around position 37.85 Mb on chromosome 4 (Bonferroni corrected p = 2.25 × 10−6 to 5.99 × 10−6). Using imputed sequence data within the interval between 32.85 and 42.85 Mb, the same region with the strongest association was identified around 37.85 Mb [See Additional file −12) was located in an intron of the PCLO gene. Additional candidate genes were identified within this 10-Mb region that contained intronic SNPs with p-values less than 2.51 × 10−7, among which were the SEMA3D, HGF, a novel gene ENSBTAG00000019276, CROT and KIAA1324-like genes. Two missense variants located in the PCLO gene and one in the KIAA1324-like gene had a p-value less than 0.005. SIFT scores were not available for the missense variants in the PCLO gene but the variant within the KIAA1324-like gene was classified as tolerated (SIFT = 0.38).Maternal calving difficulty describes the characteristics of the dam giving birth  and its impact on the parturition process. We found that maternal calving difficulty is influenced by many polymorphisms, each of small effect, since the maximum proportion of genetic variation accounted for by any one SNP was 0.13 % (BovineHD1900018551 on chromosome 19 for the Holstein-Friesian population). Several genomic regions associated with maternal calving difficulty in the Holstein-Friesian population were primarily identified on chromosomes 1, 4, 11, 13, and 19  . SSR analysis also detected associations on chromosomes 9 and 18 but after adjustment for multiple-testing, none remained significant. Six candidate genes  were identified in this region on chromosome 5.Perinatal mortality in cattle is defined as calf mortality shortly before, during, or after parturition within a 24-h period. Since no maternal genetic effects are estimated in the national genetic evaluations, our analyses are based on PTA for direct mortality. Both SSR and Bayesian analyses detected the strongest associations on chromosomes 4 and 26 for the Holstein-Friesian breed  gene [CEACAM18 gene [SIGLEC12, CTU1 and ZNF615) within this region could lead to the identification of causal mutations. Their impact on protein function was predicted and, although results should be interpreted with caution because of the low specificity of Polyphen and SIFT [CTU1 gene is the most likely candidate for direct calving difficulty in the Holstein-Friesian population. The relevance of CTU1 in calving performance is unknown but it has been suggested that deletion of CTU1 may affect proteins with a distinctive codon usage enriched for the AAA, GAA and CAA codons [CTU1 gene may have an impact on regulatory proteins or genes associated with direct calving difficulty. Furthermore, the association of intergenic variants on chromosomes 6 and 10 with direct calving difficulty suggests that unidentified gene products such as non-coding RNAs, promoters, or enhancers may affect calving performance. Further work is needed to determine the exact causal mutations for direct calving difficulty in the Holstein-Friesian population.Our finding that SNP ARS-BFGL-NGS-109285 located on chromosome 18 was the most strongly associated SNP with direct calving difficulty in the Holstein-Friesian population in both the SSR and Bayesian analyses confirms previous analyses on two separate dairy populations genotyped at a lower density , 29 and C5) gene  and the M18 gene . The detand SIFT , the mutA codons . Thus, amyostatin gene, which contributes to muscle hypertrophy. It has been clear for a long time that myostatin is associated with calving difficulty and homozygous animals for the double muscle mutation have a 19 % greater risk of calving difficulty [The strong association for direct calving difficulty in both Charolais and Limousin populations that was detected on chromosome 2 (between 5.6 and 6.6 Mb) suggests that this region contains a QTL for this trait in beef breeds. Eleven candidate genes were identified within this 1-Mb region including the fficulty  than hetfficulty . HoweverOverall, the identification of polymorphisms that accounted for a large proportion of the genomic variation in direct calving difficulty across all breeds suggests that genomic selection algorithms should facilitate the modelling of alleles with small as well as large effects. It has been reported that Bayesian selection methods may be more suited to model the underlying genomic architecture of direct calving difficulty since they facilitate the modelling of heterogeneity in SNP contributions to the genetic variance .In spite of the small size of the population analysed for maternal calving difficulty, our results suggest that this trait may be influenced by many polymorphisms each of small effect since the maximum proportion of the genetic variation accounted for by any one SNP was 0.13 %. Accuracy of genetic merit prediction has been shown to be greater for traits that are affected by SNPs with large effects, than for traits that are affected by many SNPs with small effects . Thus, aA clear association between a region on chromosome 4 and maternal calving difficulty in the Holstein-Friesian population was detected, but for the beef breeds, we found no significant regions after adjustment for multiple-testing. This may be due to the smaller number of animals analysed for this trait (221 Charolais and 357 Limousin). However, the unadjusted significant SSR associations for the Charolais and Limousin breeds were positioned near or located at the same positions as previously reported QTL associated with maternal calving ease, birth weight, or calf size on chromosomes 3 and 2, respectively , 35, 36.PCLO gene were detected, which suggests that this gene, or the genes in close proximity are involved in maternal calving difficulty. The PCLO gene is involved in human developmental malformations [The strongest SSR association that was detected using both Holstein-Friesian high-density genotype data and imputed sequence data on chromosome 4, was located 4.3 Mb away from a QTL, which was previously associated with maternal calving difficulty in Danish and Swedish Holstein cattle . Althougrmations . Other mrmations , which sPDYN on chromosome 13 and maternal calving difficulty was present in the three breeds, which is consistent with the intuitive perception that calving difficulty is one of the most painful conditions a cow can experience. Indeed, PDYN was shown to be linked to behaviour, pain perception, and psychological processes, and expression or up-regulation of this hormone serves as a biological indicator of stress in cattle [Furthermore, the meta-analysis results revealed that the association between n cattle , 40. DetSLC20A1 and UST), were previously shown to be mainly involved with morphological abnormalities [The identification of several weak genomic associations with direct perinatal mortality suggests that the infinitesimal model hypothesis may indeed hold for this trait. Therefore, to achieve genetic improvement, we suggest analysing all loci collectively when assessing perinatal mortality since the trait is influenced by the cumulative effect of thousands of loci. The genes that were found to be associated with perinatal mortality in this study (malities , 42, whiWe identified several breed-specific associations of moderate effect for calving performance due to either different breed allele substitution effects or to varying linkage phase between the genotyped allele and the causal mutation in each breed, which may have implications for across-breed genomic evaluations. The substantial difference in direction of allele effects between breeds may be the reason why across-breed genomic evaluations have been elusive until now . In addiCTU1 gene may contribute to the genetic variance of this trait. In addition, although the strongest associations for calving performance differed between breeds, genomic associations common to the three breeds were identified, which indicates that a portion of the genomic variation that is attributed to calving performance is common to the three breeds, which may have implications for across-breed genomic evaluations.Several strong associations for three calving performance traits in Holstein-Friesian, Charolais and Limousin populations were detected, although breed-specific SNP effects exist. These traits are polygenic traits since they are under the control of a large number of SNPs, each SNP being weakly associated with calving performance. Several previous studies have suggested the presence of a causal mutation on chromosome 18 for direct calving difficulty in dairy cattle. Although we did not detect any definitive mutation, a missense variant in the