High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles . High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae. A high-quality, mostly error-free genotypic data set is imperative to obtain reliable results in many downstream genetic analyses. The results of genetic analyses can be influenced by even low rates of genotyping errors . For exaThere are several reasons for the occurrence of errors in a genotypic data set. Incorrect information about a sample’s identity, e.g., due to mixing up or mislabeling samples, causes an individual to be matched with the wrong data . In clonSNPs have become the genetic marker of choice for many genetic analyses but, with their increased use and increasingly large numbers that can be generated, manual data curation has become more challenging. SNPs are ubiquitous within the genome and allow for simultaneous screening of many thousands of polymorphic loci via SNP arrays, Genotyping-By-Sequencing, or resequencing ,26. SNP For SNP arrays, incorrect genotype assignment using automated SNP scoring software occurs when intensity plots deviate from expected patterns. Automated genotyping is based on the association of specific alleles to different fluorescent molecules, the detection of these fluorescent molecules, the clustering of individual-marker data points according to intensity ratios between the different fluorescent dyes across multiple individuals into distinct regions of a genotype-calling space, and the final assignment of these clusters to genotypes. Examples of deviations that are observed in the intensity plots are the presence of additional clusters or clusters that have shifted from their expected location in the intensity plot. The presence of additional clusters or shifted clusters can be attributed to additional regions that bind to the SNP’s probe . SequencNo systematic workflow exists to efficiently detect and resolve all types of errors from a genotypic data set for pedigreed germplasm. Methods and software exist to tackle specific types of errors. For example, the ASSIsT software was developed for use with Illumina Infinium arrays to identify which SNPs show robust results, which SNPs might have genotype calling errors due to alleles with reduced affinity or null alleles, and which SNPs are monomorphic or failed completely . AnotherHere we describe a curation workflow for high-resolution genetic marker data that identifies and resolves errors to obtain a robust set of genotypic data. The workflow maximizes the genotypic data obtained from high-throughput genome-scanning tools while minimizing the time needed to identify and remove errors. The workflow resulted from curation needs in the multi state and multi-crop USDA-SCRI project RosBREED –40 and twww.rosbreed.org) and consisted of 421, 426, and 269 individuals for apple, peach, and sweet cherry, respectively , the genotype calls of 18 additional apple individuals genotyped with the 20K SNP array in the FruitBreedomics project  or genotInitial parentage information was collected as part of the germplasm creation as described by Peace and co-workers (2014) . For eacDNA extraction was conducted for apple, peach, and sweet cherry as described by Chagné and co-workers (2012) , Verde ain silico) SNPs throughout the genome. A subset of putative SNPs for each crop was first validated using the Illumina GoldenGate assay to fine-tune the filter parameters used in the final choice of SNPs for the array. In apple, 27 individuals were sequenced and their sequence was aligned to the ‘Golden Delicious’ apple reference genome v1.0 , that cannot be readily resolved other than by removal of these specific individuals.The histogram function in GenomeStudio enabled quick identification of polyploids and individuals with very poor DNA samples without the need for additional steps in Excel, R, or other software. However, identification of aneuploids and individuals with potentially low-quality DNA samples was not as straightforward. Plotting the B-allele frequency against physical or genetic marker order (when available) required additional data manipulation and generation of the plots in software outside GenomeStudio, but most of it could be automated using R and custom scripts. Therefore, we suggest using GenomeStudio for initial removal of poor-quality samples and polyploids, and afterwards, when positional information for the markers is available, screening for aneuploids with the method described by Chagné and coworkers (2015)  Table 4Table 4.SNPs with major scoring issues that cannot be easily resolved manually need to be removed from the data set. The early detection and removal of these unreliable SNPs greatly reduces the number of marker and map errors reported, as well as the time spent evaluating these SNPs in later workflow stages. By using ASSIsT, a quick subset of SNPs with robust genotype calls could be generated. On average across the three crops, 80% of this subset was retained in the final data set, which is lower than the 99% for single full-sib families that was reported by Di Guardo and co-workers (2015) . As the 1 families rather than in all families and across generations. However, many SNPs with null alleles that were later identified in the workflow could be accurately genotyped manually as long as homozygous ‘AA’ and ‘BB’ individuals could be distinguished from individuals that carried a null allele. This distinction was time-consuming and therefore we recommend saving these SNPs only when it justifies the time needed to do so. Examples when such markers can be of high value are in the construction of genetic linkage maps, even if multiple mapping populations are used [Markers with null alleles identified by ASSIsT were removed from the data set, as they could only be identified and automatically called in specific Fare used , when thVery few other options exist to create a subset of high-quality genome-wide markers across pedigreed germplasm. GenomeStudio does provide several quality scores that have been used before in SNP filtering, but no guidelines exist on what threshold values to use. Using parameter thresholds regularly reported in literature ,56–59 G.As thresholds to confirm or discard historic pedigree information depends on the germplasm, genotyping platform, and data quality, they need to be assessed case-wise. A custom R-script provided quick and easy determination of the number of PC and PPC errors. However, the custom code required a significant amount of time to identify possible parents when one or both parents were unknown, especially for larger data sets. Similar issues were observed for Cervus, which took a long time to run (days) and did identify some incorrect relationships, especially for inbred material. Cervus also requires a specific data format and we experienced some problems running the software for large data sets that were not immediately resolved. GenomeStudio provided the quickest way to determine the number of PC and PPC errors, which could be determined immediately after loading the raw intensity data. However, new PC relationships could not automatically be determined and only SNPs retained by ASSIsT should be used when using GenomeStudio to determine the number of PC errors, to avoid inflating the number of PC errors. Therefore, we recommend using GenomeStudio to confirm existing pedigree records when using Illumina arrays and using an R-script to determine new, previously unknown, PC relationships . Time-coIndividuals with only one parent known can still be used in a pedigree-based approach to find errors in the data set, although some errors might remain unnoticed. We recommend using the ‘M_’ and ‘F_’ prefixes to the individual’s name to designate the unknown mother or father, respectively. When it is unclear whether the unknown individual is the mother or the father, the ‘UP_’ prefix can be used. Using this system instead of a non-descriptive name such as ‘dummy 1’ creates a clear connection between the individual with an unknown parent and the placeholder individual that is introduced. When the correct parent is later found, it also allows the quick replacement of the placeholder by the correct name (and corresponding genotypic data). Use of the same name for any missing parent should be avoided  unless the missing parent is unequivocally the parent of multiple individuals. If the same name is used incorrectly for multiple missing parents, the genotype of that single missing parent is expected by FlexQTL to be consistent with inheritance principles for all of its assigned offspring, potentially creating a large number of errors in further steps.Although non-diploid individuals should be removed from the workflow before identifying reliable SNPs, they can have their pedigree checked if needed. Regardless of their ploidy, individuals should only contain alleles that are present in their parents. For example, a triploid individual with a marker call at one SNP of ‘AAA’ will be scored as ‘AA’, but can still not have a ‘BB’ parent. However, caution is advised as the grandparents through the parent that provided the unreduced gamete will also share a full allele set with any polyploid individual and thus these grandparents could also be incorrectly assigned as a parent of the polyploid individual. For example, the triploid ‘Zonga’ and its (diploid) grandparent ‘Cox’s Orange Pippin’ share a full allele set (through an unreduced gamete of ‘Alkmene’) and thus no PC errors are reported . HoweverThis study used available genetic maps for apple and cherry  with the true recombination. Making genotype calls missing for the individual(s) with a recombination in that area enlarged the recombination interval for those individuals, but also led to correct phasing in their parent and resolved the supposed double recombinations in their full- and half-sibs. Very few other phasing issues were observed that could not be resolved on a single SNP level but were later resolved at the haploblock level. Thus, a small number of phasing issues can be accepted when moving forward to generating haploblocks and they could be nullified by FlexQTL by setting the parameter ‘DeleteDR’ to 1.Visual FlexQTL showed good accuracy  in determining haploblock borders based on historic recombination events. Two reasons exist for not identifying all historic recombinations for haploblock border determination. First, Visual FlexQTL determines the border as the middle of the recombination interval. The more non-informative markers present in the recombination interval (due to homozygosity or lack of co-segregation (phase) information), the less likely that the middle position is the true position of the historic recombination (which determines the haploblock border). Secondly, FlexQTL determines haploblock borders sequentially, starting with small recombination intervals; if multiple recombinations occur in the same region, one haploblock border could suffice to account for all recombinations. This approach thus minimizes the number of recombination sites needed to explain observed segregation data. In reality, the recombinations could have occurred between different markers, requiring that region to be split in additional haploblocks to avoid recombination within haploblocks for selected material.PediHaplotyper’s haplotypes did not always match with SNP data. In most cases, these inconsistencies were introduced during the marker consistency check with FlexQTL to ensure the haplotypes in an individual matched those of its parents and offspring. When the haplotype that caused the inconsistency was represented well in the pedigree, the haplotype was correct and the original genotype call for the SNP was incorrect. Thus, in these cases, haplotype curation identified additional errors in the SNP data. These errors were mostly caused by (very) small incorrectly identified genotype clusters or by single calling errors in the data set that were not detected earlier. When haplotypes in poorly represented individuals  showed an inconsistency with the SNP data, the SNP data was mostly correct and an error had occurred during haplotyping. The error could span multiple generations leading to inconsistencies for multiple individuals but its impact on the data set was small as the overall representation of the incorrect haplotype was small. In the rare case that a poor representation led to incorrect haplotype determination, the actual cause of the inconsistency often remained unclear, but for some it was due to a recombination within a haploblock for an un-genotyped ancestor or one of the direct parents of such an ancestor.Haploblock borders are not fixed and can change based on the application of the final data set and the germplasm used. For example, for QTL analyses some of the haploblocks defined here will be too large as they span multiple cM; they will show within-haploblock recombination in numerous unselected offspring thereby increasing the number of missing haplotype calls thus increasing uncertainty in QTL position . Haploblock sizes can therefore be reduced to minimize within haploblock recombination and better define QTL regions. However, when haploblocks are very small, many haploblocks will consist of only one SNP or a few SNPs, increasing data sizes  and reducing the number of haplotypes per haploblock, which can reduce the suitability of the data for visual examination. Unlike the 8K apple SNP array, the 20K apple SNP array was designed to have clusters of multiple SNPs spread at approximately 1 cM intervals. A similar approach was used to create 9K add-ons for the 9K peach array and 6K cherry array . This stDifferent germplasm will also lead to different haploblock borders. Currently, haploblocks are based on historic recombination events representing the U.S. breeding programs included in this study. Other breeding programs or genetic studies might have other sets of founders and thus different recombinations of relevance. Furthermore, the addition of new advanced selections and parents will introduce new recombinations in their germplasm. Finally, as the understanding of the apple, peach, and cherry germplasm increases, previously unknown progenitors, founders, and pedigree connections will be discovered, also increasing the number of observed recombinations.Given that haploblocking is performed at a relative late stage in the workflow, haploblock borders can be altered without the need to redo all previously conducted pedigree and SNP marker curation. In fact, existing haplotype data can be converted back to phased, fully curated SNP data which, in turn, can be used to determine haplotypes for any set of haploblocks. As the SNPs are already phased and missing SNP data was imputed based on the haplotypes, haplotype determination for new haploblock borders should not create new genotyping errors in the data set. Once numbers of new recombinations are high enough to justify updating of haploblock data, part of the haploblocks and their haplotypes should be altered. PediHaplotyper supports the use of previous haplotype definitions for haploblocks that did not change in composition. Adjusted haploblocks could be marked through their names, thus providing tools to monitor new as well as previous, possibly well-known, marker alleles.The established SNP classification system enables the quick creation of a subset of SNPs that require minimal or no data curation and provides a guideline on possible issues with other SNPs and how to solve them. The system should help with the quick integration of new genotypic data into existing data sets. Genotype calls for SNPs of type 1 and type 2 can be quickly integrated with high confidence in their genotype calls. Where desired, SNPs of type 3, 4, and 5 can also be integrated, but additional curation would be required. Depending on germplasm tested, these SNPs might have incorrect genotype scoring but their SNP type is an indication of why the genotype scoring is wrong and how to fix it. In other germplasm, additional SNPs in the probe or null alleles might not be present, causing SNPs that are now classified as type 4 or type 5 to give reliable results as if they were type 1 or type 2. Similarly, if germplasm is used that is unrelated to that used here, type 1 and type 2 SNPs might show additional clusters or null alleles and will require further curation. Finally, type 7 SNPs, which could not be mapped in this germplasm, might be mapped and valuable for other germplasm.www.rosaceae.org), combined with the SNP classification system, will facilitate correct curation of additional genotypic data, even if the new germplasm is not directly descended. The SNP genotype calls provided here are a reference for the genotype of each observed genotype cluster in GenomeStudio. In addition, SNP cluster coordinates of the latest GenomeStudio file can be imported into new projects, thus helping GenomeStudio to correctly identify clusters. Finally, the use of reference iScan data is especially useful for markers that have only two of the three clusters in a new project but all three clusters were defined in the current reference data set. By adding reference iScan data into the new project, all three clusters will be available, ensuring correct automated genotype calling. Therefore, we recommend including available reference data when obtaining genotype calls for new germplasm.The available reference data , which were too small for FlexQTL to accurately determine linkage phase. For parents with just one genotyped offspring, phasing of the parent homologs was considered putative as recombination inherited by offspring could not be determined. For those parents with just two genotyped offspring, recombinations were arbitrarily assigned between the two offspring, as the true recombinant offspring could not be determined. In addition, scarce information on pedigrees in ancestral generations beyond about five limited further imputations in data curation, unlike for apple and peach. Various founders showed extensive regions of common haplotypes, indicating a high degree of relatedness among such founders. Some recently published haplotyping results exemplify this for the founders ‘Black Republican’ and ‘Napoleon’ . UnravelThe proposed workflow could be applied to other diploid crops with similar breeding systems where clonally propagated relatives of current breeding material still exist. However, there are additional aspects that would need to be considered in certain circumstances that were not encountered in the present study. First, this workflow makes the assumption that there are no differences in the true SNP map order among individuals of a species. In interspecific crosses where there can be differences in chromosome arrangements between parental species, the different SNP order or indel variation among individuals could result in additional perceived double recombinations or other difficulties in following this workflow. Additionally, this workflow assumes that there is sufficient marker information to correctly identify pedigree relationships and assumes sufficient segregation information for validating marker order and identifying Mendelian-consistent errors. When using highly homozygous, inbred individuals, there might be too few segregating markers available to correctly identify marker order or find Mendelian-consistent errors through double recombinations. Also, for small germplasm sets, too few recombinations might be available to detect incorrect marker order. Finally, the prevalence of missing genotypic values should be sufficiently low across individuals. Unlike the SNP arrays used in this study, some genotyping methods such as Genotyping-by-Sequencing do not consistently target specific loci. This non-specificity can increase the flexibility of their use, but also raises new issues for which the current workflow would have to be adapted, including the potential decrease in accuracies of genotyping and haploblock determination due to unbalanced representation of genotyped loci, high levels of missing data, and sequencing errors.www.rosaceae.org).The presented genome-wide genotypic data sets for apple, peach, and sweet cherry are of very high quality, are composed of genetically complex germplasm, and contain no errors that could be determined based on pedigree information. This high quality provides confidence in the results of downstream analyses. Such confidence is important as many of these results are expected to lead to fundamental discoveries and practical breeding application. The iScan data, phased SNP, and haplotype data sets of individuals in the apple, peach, and sweet cherry crop reference sets are available through the Genome Database for Rosaceae , as well as used on apple and peach germplasm sets representing European breeding programs based on the apple 20K and peach 9K arrays, whose SNP data are still private. These high-quality data sets contain the largest sets of SNPs obtained through their respective SNP arrays and will provide the foundation for confident subsequent analyses in genetic research.A curation workflow for genotypic data of pedigreed germplasm was generated by determining the optimal order of resolving issues and by providing a step-by-step guideline. Using simple genetic principles, errors can be found and curated in a directed and efficient way, reducing the time needed to obtain a high-quality genotypic data set. The workflow was used to obtain a SNP data set for large germplasm sets for each of apple, peach, and sweet cherry representing U.S. breeding programs based on the apple 8K SNP array, peach 9K SNP array, and cherry 6K SNP array, respectively, whose SNP data is available through this paper  or the FruitBreedomics project. Except for the Breeding Pedigree sets, curated pedigree information is given for each individual. For each individual, the type of material (selected vs. unselected), the location of sampling, quality of the results, and inferred ploidy of the sample are given. For unselected seedlings, the family to which they belong is also given. For the Breeding Pedigree Sets, this information is summarized per full-sib family. If tissue was collected at the USDA germplasm repository in Geneva, a GRIN accession number is also provided. Parents highlighted in yellow did not have genotypic data and their pedigree-relationships could not be tested.(XLSX)Click here for additional data file.S2 TableIndividuals are split over the publicly available RosBREED Crop Reference Set and three privately held RosBREED Breeding Pedigree Sets. Except for the Breeding Pedigree Sets, curated pedigree information is given for each individual. For each individual, the type of material (selected vs. unselected), the location of sampling, and quality of the results of the sample are given. For unselected seedlings, the family to which they belong is also given. For the Breeding Pedigree Sets, this information is summarized per full-sib family.(XLSX)Click here for additional data file.S3 TableAll individuals are part of the publicly available RosBREED Crop Reference Set. For each individual, curated pedigree information, the type of material (selected vs. unselected), the location of sampling, and quality of the results of the sample are given. For unselected seedlings, the family to which they belong is also given.(XLSX)Click here for additional data file.S4 TableParameter settings used for (A) filtering SNPs used in analyses of B-allele frequency, (B) running ASSiST, (C) running FlexQTL for detecting Mendelian-inconsistent errors and Mendelian-consistent errors, and (D) running FlexQTL for phasing, haploblock determination, and creating PediHaplotyper input files.(XLSX)Click here for additional data file.S5 TableFor each marker, genetic position, associated haploblock, and physical position based on the apple GDDH 13 v1.1 genome are given.(XLSX)Click here for additional data file.S6 TableFor each marker, genetic position, associated haploblock, and physical position based on the peach v2 genome are given.(XLSX)Click here for additional data file.S7 TableFor each marker, genetic position, associated haploblock, and physical position based on the peach v2 genome are given.(XLSX)Click here for additional data file.S8 TableEach SNP is classified as follows: Type ‘1’ for SNPs with good clustering and less than 5% call errors, ‘2’ for SNPs with shifted clusters causing one of the clusters to be called incorrectly, ‘3’ for SNPs with additional clusters  that cause the incorrect identification of at least one cluster, ‘4’ for SNPs with null-alleles that cannot be correctly called automatically, ‘5’ for SNPs that could not be mapped accurately but had correct clustering, ‘6’ for monomorphic SNPs, and ‘7’ for failed SNPs.(XLSX)Click here for additional data file.S9 TableEach SNP is classified as follows: Type ‘1’ for SNPs with good clustering and less than 5% call errors, ‘2’ for SNPs with shifted clusters causing one of the clusters to be called incorrectly, ‘3’ for SNPs with additional clusters  that cause the incorrect identification of at least one cluster, ‘4’ for SNPs with null-alleles that cannot be correctly called automatically, ‘5’ for SNPs that could not be mapped accurately but had correct clustering, ‘6’ for monomorphic SNPs, and ‘7’ for failed SNPs.(XLSX)Click here for additional data file.S10 TableEach SNP is classified as follows: Type ‘1’ for SNPs with good clustering and less than 5% call errors, ‘2’ for SNPs with shifted clusters causing one of the clusters to be called incorrectly, ‘3’ for SNPs with additional clusters  that cause the incorrect identification of at least one cluster, ‘4’ for SNPs with null-alleles that cannot be correctly called automatically, ‘5’ for SNPs that could not be mapped accurately but had correct clustering, ‘6’ for monomorphic SNPs, and ‘7’ for failed SNPs.(XLSX)Click here for additional data file.S1 FigHigher call rates are observed for individuals with better quality of genotypic data.(DOCX)Click here for additional data file.S2 FigSNP B-allele frequencies plotted against physical position in the genome for (A) triploid individuals excluding ‘Jonagold’, and (B) individuals with a tetraploid pattern.(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(R)Click here for additional data file.S3 File(R)Click here for additional data file.S4 File(PDF)Click here for additional data file.


Ne) for the purebred  and crossbred  buffalo populations using the 90K Buffalo SNP genotyping array. The results showed that the average square of correlation coefficient (r2) between adjacent SNP was 0.13 ± 0.19 across all autosomes for purebred and 0.09 ± 0.13 for crossbred, and the most rapid decline in LD was observed over the first 200 kb. Estimated r2 ≥ 0.2 extended up to ~50 kb in crossbred and 170 kb in purebred populations, while average r2 values ≥0.3 were respectively observed in the ~10 and 60 kb in the crossbred and purebred populations. The largest phase correlation  was observed at the distance of 100 kb, suggesting that this phase was not actively preserved between the two populations. Estimated Ne for the purebred and crossbred population at the current generation was 387 and 113 individuals, respectively. These findings may provide useful information to guide the GS and GWAS in buffaloes.Linkage disequilibrium (LD) is a useful parameter for guiding the accuracy and power of both genome-wide association studies (GWAS) and genomic selection (GS) among different livestock species. The present study evaluated the extent of LD, persistence of phase and effective population size ( Genomic selection (GS) has been widely used to estimate the breeding values in various fields, such as animal and plant breeding programs . The Ne that is estimated using the r2 coefficient has been an explosion of interest in the application of population genetics  of BovineHD chip, which corresponded to an average genomic distance of 80 kb. Existing evidence revealed that the average r2 value of 0.20 was considered enough to achieve an accuracy of >0.80 for GBVs estimation  is a dual-purpose (milk and meat) livestock across the world that can generally be divided into two subspecies: River (2n = 50) and Swamp (2n = 48) buffalo. These animals were domesticated 3,000–6,000 years ago, and the independent domestication events occur in swamp buffalo from China and the river buffalo from the Indian subcontinent   is the only commercial SNP genotyping array that can be utilized for obtaining the genome-wide SNP data in buffalo. Using this SNP genotyping array, moderate r2 levels (0.20–0.32) were observed in Brazilian buffalo for greater distances (10–70 kb)  buffalo, n = 36], were used in the present study. The purebred was selected from four herds in the Southern part of Italy, while the crossbred animals were chosen from two herds located at the Hubei Jinniu farm and Guangxi Buffalo Research Institute, respectively. The crossbred individuals were 3-way cross buffaloes and selected by the pedigree information against full- or half-sib animals. Genomic DNA was isolated from the blood sample using the standard phenol-chloroform extraction protocol. Genotyping was conducted at the Delta Genomics  using the Axiom® Buffalo SNP Genotyping Array. Quality control (QC) was performed using PLINK v1.90  from Hardy-Weinberg Equilibrium (HWE). For the studied populations, principal component analysis (PCA) was used to estimate the population admixture using the R  and crossbred (n = 45) buffaloes after QC were included for further analysis.A total of 495 buffaloes, including 430 purebred Mediterranean and 65 Chinese crossbred buffaloes  fully informative SNPs to saturate the genome at an average resolution of 100 kb based on the bovine genome size (2.87 Gb). Here, our data suggested that a minimum of 47,000 [2.83 Gb/60 kb at LD (r2) = 0.3] SNP markers, calculated based on the buffalo genome size of 2.83 Gb approximately  dropped to < 0.2 at distances between SNPs of ~50 kb in the crossbred population, whereas this drop in the purebred was observed for much greater distances (~170 kb) . A similar result for the extent of LD was found between the crossbred beef cattle and purebred Angus and Charolais cattle  = 0.2] is required for the genomic selection analysis in buffaloes. Similarly, Cardoso et al. (r2 levels (0.20–0.32) at the marker distances of 10 ~ 70 kb in Brazilian buffaloes using the buffalo 90K SNP genotyping array, which is consistent with that of our data for the purebred population. Alternately, although at least 20.68% of adjacent SNP pairs had an r2 > 0.2 and 13.42% had an r2 > 0.3 in the purebred population, only 7.02% of adjacent SNP pairs of the 90K SNP panel showed an r2 > 0.3 in the crossbred population. Therefore, our data suggested that a higher density SNP array were required for the implementation of GS in the Chinese crossbred buffalos.Some critical average y et al.  reportedo et al.  observedr value represents the degree of genetic relationship between two populations  observed at the distance of < 100 kb. This finding indicated that the phase might be not actively preserved between breeds. Based on this finding, we assume that to increase the markers density in the array is an alternative solution for increasing the correlation of LD phase, and an adequate representation of each breed needs to be employed in the reference population when considering the application of GS in a multi-breed training population.Understanding the persistence of LD phase is another essential strategy for GS across breeds or population because SNPs pairs can exhibit the difference of LD phases between two populations .TD, AL, JL, MC, BG, AS, ZW, CZ, GP, and GC collected the buffalo blood samples. TD conceived and carried out the analysis, interpreted the data and wrote the manuscript. TD and LY conceived the study and participated in its design and coordination. GH, TY, SL, HR, XL, and LY reviewed the paper. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The electrocardiographically quantified QRS duration measures ventricular depolarization and conduction. QRS prolongation has been associated with poor heart failure prognosis and cardiovascular mortality, including sudden death. While previous genome-wide association studies (GWAS) have identified 32 QRS SNPs across 26 loci among European, African, and Asian-descent populations, the genetics of QRS among Hispanics/Latinos has not been previously explored.We performed a GWAS of QRS duration among Hispanic/Latino ancestry populations  from four studies using 1000 Genomes imputed genotype data . Study-specific results were combined using fixed-effects, inverse variance-weighted meta-analysis.P<5x10-8), including two novel loci: MYOCD, a nuclear protein expressed in the heart, and SYT1, an integral membrane protein. The top SNP in the MYOCD locus, intronic SNP rs16946539, was found in Hispanics/Latinos with a minor allele frequency (MAF) of 0.04, but is monomorphic in European and African descent populations. The most significant QRS duration association was with intronic SNP rs3922344 (P = 1.19x10-24) in SCN5A/SCN10A. Three other previously identified loci, CDKN1A, VTI1A, and HAND1, also exceeded the GWAS significance threshold among Hispanics/Latinos. A total of 27 of 32 previously identified QRS duration SNPs were shown to generalize in Hispanics/Latinos.We identified six loci associated with QRS (Our QRS duration GWAS, the first in Hispanic/Latino populations, identified two new loci, underscoring the utility of extending large scale genomic studies to currently under-examined populations. The duration of the QRS complex on a resting, standard 12-lead electrocardiogram (ECG) represents the electrical depolarization of the ventricles as an impulse travels through the cardiac conduction system and the ventricular myocardium. Delay in cardiac ventricular conduction results in increased QRS durations, and has been shown to predict heart failure prognosis, and cardSCN5A, SCN10A, TBX3, TBX5, TBX20, and HAND1.[To date, heritability estimates of QRS duration have varied, with up to ~40% heritability found in more recent studies.–9 While nd HAND1., 16–18 TOur GWAS included 15,124 Hispanic/Latino individuals from four contributing cohorts. Baseline characteristics varied substantially across the four cohorts. For example, WHI is a study of women only. The average age of the participants across the four cohorts ranged from 45 to 61 years. The prevalence of hypertension and diabetes across the four cohorts ranged from 26% to 42% for hypertension and 8% to 46% for diabetes .MYOCD and SYT1) were novel, whereas the remaining four  were previously identified in other ethnic groups. There was no evidence of heterogeneity  associated with QRS duration. The index SNP (rs16946539) in MYOCD , as well as the only SNP (rs139859815) in high LD (r2>0.5) with it, are monomorphic in the European-descent and African-descent 1000 Genomes super populations , an integral membrane protein of synaptic vesicles that responds to calcium signaling. The index SNP in SYT1 (rs4842438) was examined in both European and African ancestry GWAS efforts, but failed to reach nominal significance , and is closer to the effect size seen among African Americans  with rs62241190 and rs9856387. Therefore, whether these SNPs are also significantly associated with QRS duration among non-Hispanic/Latino groups is unknown.The most significant association with QRS duration was found in chromosome region 3p22 (rs3922844) at locus 1, bridging el genes . This SNel genes  Tables. el genes . In cont0.94 ms,  Tables.  signals . Two of CDKN1A and VTI1A, and an intergenic SNP near HAND1. While the Hispanic/Latino index SNP in CDKN1A (rs3176326) was not directly evaluated in the European or African American GWAS, it was in high LD with a SNP that had been found to be highly significant in the previous European ancestry GWAS (r2 = 0.71 with rs9462210). Interestingly, a conditional analysis of the CDKN1A locus revealed a suggestive secondary signal located in an intron of SPRK1 approximately 1 Mb upstream from the primary CDKN1A signal  is a novel SNP within a known locus, but it is not in high LD with the previously known SNP associated with QRS duration in Europeans (HAND1 (rs13165478) show that five SNPs in the region  were significantly associated with QRS duration in this 1000 Genomes imputation analysis. No other SNPs had near significant associations. This same haplotype was also identified as significant among European descent individuals  for association with QRS duration among Hispanics/Latinos, 13, 18 2>0.9) with the index Hispanic/Latino SNP, and one was monomorphic in European and African-descent populations. After Bonferroni correction, 4 of the 29 independent SNPs showed evidence of significant differences in genotype-phenotype effect sizes . Intriguingly, the novel MYOCD locus was significantly associated with PR duration at the Bonferroni corrected significance level, with the SNP that prolongs QRS duration but shortens PR duration. None of the QRS index SNPs were significantly associated with heart rate or SDNN.The 9 index SNPs identified in the Hispanic/Latino QRS duration GWAS were also examined for their association with other ECG phenotypes: QT duration, PR duratSCN5A-SCN10A, CDKN1A, and MYOCD. SNPs in CDKN1A showed evidence for activating transcription in heart tissues, including fetal heart tissue, the right atrium, the right ventricle, and the left ventricle. SCN5A-SCN10A and MYOCD SNPs were identified as possible enhancers of transcription in the same heart tissues , and four loci  were previously identified in QRS GWAS analyses of European[Our GWAS meta-analysis of four cohorts  of Hispanic/Latino ethnicity found 9 index SNPs across 6 loci with genome-wide significant associations with QRS duration. Two loci were novel  expressed in cardiomyocytes and smooth muscle cell-containing tissues. MYOCD has been shown to be essential for maintaining adult heart function.[MYOCD is postnatally knocked down develop dilated cardiomyopathy and fatal heart failure.[MYOCD was associated with left atrial size.[MYOCD associated with PR interval duration[MYOCD (rs16946539) is more common among Hispanics/Latinos  than among any other 1000 Genomes super population, and is monomorphic among European- and African-descent individuals . Therefore, a QRS GWAS in Hispanics/Latinos is uniquely advantageous in uncovering this genotype-phenotype association. The finding illustrates one of the main imperatives for conducting genetic studies in diverse and under-examined populations, both for ECG traits, and for other traits. Because underlying genetic architecture can differ across racial and ethnic populations, examining new populations may uncover novel genotype-phenotype associations.We identified two novel loci associated with QRS duration among Hispanic descent individuals: function. Mice in  failure. A genetiial size. Recent G duration and atri duration in EuropSYT1. Interestingly, a different synaptotagmin gene, SYT10, was previously associated with heart rate.[SYT1 intronic SNP was not associated with heart rate in our study. It is noteworthy that this SYT1 SNP showed no evidence of association with QRS among European and African-descent individuals. Further studies are warranted to validate this novel association in additional Hispanics/Latinos populations.The second novel locus found in this study involves an intronic SNP in art rate. The SYT1SCN5A and SCN10A. Whereas SCN5A is the canonical cardiac sodium channel responsible for cellular depolarization and enables conduction of the electrical signal, SCN10A appears to be particularly enriched in the specialized His Purkinje conduction fibers.[The most significant association signal was found in and surrounding two voltage-gated sodium channel genes: n fibers. TransethThe generalization study of the 28 independent index SNPs from QRS GWAS analyses in Hispanic/Latino, European, and African ancestry individuals shows remarkable consistency in the magnitude and direction of the effects overall. Although the genetic architecture of QRS duration in the three ethnic groups is largely comparable, differences are also present, such as the larger effect size at rs3922844 among Hispanics/Latinos than among European descent individuals.NFIA locus fell just short of genome-wide significance in Hispanics/Latinos (P = 5.1x10-8). Furthermore, while we excluded individuals with QRS durations longer than 120 ms in order to exclude individuals who have conduction defects and/or bundle branch blocks due to acquired heart disease, interesting genetic associations may be missed by this approach.Several limitations deserve consideration. First, our GWAS represents the largest performed in Hispanics/Latinos, but our sample size was nonetheless small. Additional Hispanic/Latino cohort studies are needed to extend these findings. However, novel associations were identified, and known associations were confirmed in a new ethnic population, despite the relatively small sample size. Larger sample sizes may reveal more genome-wide significant loci. For example, a SNP in the previously identified MYOCD that is monomorphic in both Europeans and African Americans. Our study underscores the importance of conducting genetic studies in diverse and under-examined populations, such as Hispanics/Latinos, to uncover novel loci.In conclusion, our findings indicate that the genetics of QRS duration are largely similar among ethnic groups. However, important differences do exist, illustrated, by the novel genome-wide significant SNP in Our meta-analysis included 15,124 participants of self-identified Hispanic/Latino descent from the following four studies: the HCHS/SOL , the Multi-Ethnic Study of Atherosclerosis , the Starr County Study (n = 582), and the WHI (n = 1545)  and African American  ancestries. Details of these studies can be obtained from their original publications., 18Participants in each of the four cohorts underwent a standard 12-lead ECG by a certified technician see . ParticiHCHS/SOL participants were genotyped on an Illumina custom array that included the Illumina Omni 2.5M array (HumanOmmni2.5-8v1-1) and an additional ~150,000 SNPs. The additional SNPs were chosen to contain markers relevant to Hispanic/Latino ancestry, markers informative of Native American ethnicity, and significant loci from previous association studies. MESA, WHeffN <30), with effN = 2 x MAF x (MAF-1) x N x Imputation Quality; where N is the number of participants. Each cohort contained between ~6M and ~20M imputed SNPs, after applying this filter.To assess the association between genotype and QRS duration, individual cohort studies used additive genetic linear regression models, either in a regression model  or a mixed model . The two methods estimate the same effect. Models were adjusted for age, sex, heart rate, systolic blood pressure, BMI, height, study site/region, and principal components of genetic ancestry. After we received the results from the studies, we applied an individual cohort QC filter, which excluded SNPs with low imputation quality (<0.30) or small effective sample sizes for each individual SNP ; Bonferroni corrected significance (P<1.39E-03 for 36 tests); or nominal significance (P<0.05).Index SNPs discovered in the QRS duration analysis were also examined for associations with other ECG phenotypes, including QT, PR, heart rate, and SDNN. These GWAS efforts were based upon the same underlying Hispanic/Latino cohorts as the QRS duration GWAS. However, due to different inclusion/exclusion criteria, differences in the study samples do exist between studies. Full details of these studies can be obtained from their original publications.–23 Signi2>0.80) with these SNPs. The LD structure pattern used for this analysis was the 1000 Genomes AMR Phase-1 super-population.The HaploReg v4.1 online web resource was used to functionally annotate genome-wide significant SNPs. HaploRegS1 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.S4 Fig2) between the plotted SNP and the index SNP.Plots created with LocusZoom software. The inde(DOCX)Click here for additional data file.S5 Fig2) between the plotted SNP and the index SNP. SNPs plotted as circles were directly genotyped, and SNPs plotted as X’s were imputed.Plots created with LocusZoom software. Each plo(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


FABP3 (rs1110770079) single nucleotide polymorphism (SNP) could be a candidate gene associated with IFC (but not ABT), and IFC could be improved by selecting the individuals with a favorable genotype (GG) of FABP3 (rs1110770079) SNP for pig breeding.Appropriate intramuscular fat content (IFC) is the goal of consumers and the direction that breeders must pursue. However, it is difficult to improve the IFC but not average backfat thickness (ABT) by traditional breeding methods, and pigs must be slaughtered to accurately measure IFC. Marker-assisted selection (MAS) provides an economic and efficient method to improve the IFC in pigs. Our research indicated that the FABP3, LIPE, IGF1, IGF2, LEP, LEPR, MC4R, PHKG1, RETN, RYR1, SCD, and UBE3C. Associations of the evaluated SNPs with IFCIFC and ABT were performed. Our results showed that the means of IFC and ABT were 1.99 ± 0.03 % and 26.68 ± 0.28 mm, respectively. The coefficients of variation (CVs) of IFC and ABT were 31.21% and 19.36%, respectively. The phenotypic and genetic correlations between IFC and ABT were moderate. Only the FABP3 (rs1110770079) was associated with IFC (p < 0.05) but not with ABT. Besides, there was a tendency for associations of RYR1 (rs344435545) and SCD (rs80912566) with IFC (p < 0.1). Our results indicated that the FABP3 (rs1110770079) SNP could be used as a marker to improve IFC without changing ABT in the Suhuai pig breeding system.The present study aimed to identify the molecular markers for genes that influence intramuscular fat content (IFC), but not average backfat thickness (ABT). A total of 330 Suhuai pigs were slaughtered, and measurements of IFC and ABT were obtained. Phenotypic and genetic correlations between IFC and ABT were calculated. Thirteen single nucleotide polymorphisms (SNPs) among 12 candidate genes for IFC were analyzed, including  Meat is widely consumed as an important protein source for humans; pork occupies a dominant position in the consumption structure of meat in China , and its2 = 0.25–0.50) [FABP3 (rs1110770079) [LIPE (rs328830166) [IGF1 (rs322131043), IGF1 (rs341412920) [IGF2 (g.3072G > A) [LEP (rs45431504) [LEPR (rs45435518) [MC4R (rs81219178) [PHKG1 (rs697732005) [RETN (rs327132149) [RYR1 (rs344435545) [SCD (rs80912566) [UBE3C (rs81329544) [At present, improving IFC has become an important objective in the modern pig breeding program , especia25–0.50) , it is s0770079) , LIPE r8830166) , IGF1 r1412920) , IGF2  , LEPR  .In general, the phenotypic correlation between backfat (BF) thickness and IFC is moderate and positive. Therefore, increasing IFC often leads to increased BF thickness . The incSuhuai pig is a new lean-type pig breed. Briefly, after 23 years of successive breeding of the crossbred offspring of the Large White Pig and Huai Pig, a new breed was developed, called the Xinhuai Pig, which contains 50% Large White and 50% Huai pig (1954–1977). Subsequently, Large White pigs were again used to cross with Xinhuai pigs, and their offspring were selected and bred for 12 years to obtain a new national lean type breed called the Suhuai pig, which contains 25% Chinese Huai pig and 75% Large White (1998–2010) . At presIFC is affected by many factors, such as heredity, nutrition, environment, and feeding methods. A previous study has shown that the CV of IFC was large within Suhuai pigs . In the All experimental animals were raised according to Guidelines for the Care and Use of Laboratory Animals prepared by the Institutional Animal Welfare and Ethics Committee of Nanjing Agricultural University, Nanjing, China. All experimental protocols were approved by the Nanjing Agricultural University Animal Care and Use Committee (Certification No.: SYXK (Su) 2017-0007).Longissimus Dorsi (LD) muscle samples from the last rib of the left half of the carcasses were collected and used to determine IFC by the Soxhlet extraction method [A total of 330 Chinese Suhuai pigs were used in this study, including 247 barrows and 83 females. Experimental pigs were all raised under standard indoor conditions in Huaiyin breeding farm . Since these pigs were not raised at the same time and the same age (219.10 ± 1.10 day), they could only be slaughtered at the same slaughterhouse in 3 batches in Jinyuan Meat Products Co., Ltd.  when these pigs reached market weight (80–90 kg). Age, carcass weight, sex, batch, and pedigree were recorded in this study. n method . Ear tisLIPE (rs328830166), IGF1 (rs341412920), IGF1 (rs322131043), IGF2 (g.3072G > A), LEP (rs45431504), LEPR (rs45435518), MC4R (rs81219178), RETN (rs327132149), RYR1 (rs344435545), SCD (rs80912566) and UBE3C (rs81329544). Fragments covering these 11 SNPs were amplified using a multiplex of PCR reactions. The multiplex PCR reaction, performed in a 20 μL total volume, contained 1× GC-I buffer , 3.0 mM Mg2+, 0.3 mM dNTPs, 1 U of Hot-Start Taq DNA polymerase , 1 μL of primer mixture, and 20 ng of genomic DNA. The PCR program was as follow: 95 °C for 2 min, 11 cycles , 24 cycles , 72 °C for 2 min, and hold at 4 °C. The purification reaction, performed in a new volume, contained 10 μL of each PCR product, 5U SAP and 2U Exonuclease I at 37 °C for 1 h and at 75 °C for 15 min. The ligation reaction, performed in a 10 μL final volume, contained 1 μL of 10× ligation buffer, 0.25 μL of Taq DNA Ligase , 0.4 μL of 5′ ligation primer mixture, 0.4 μL of 3′ ligation primer mixture, 2 μL of purified PCR product mixture and 6 μL of double distilled water (ddH2O). The ligation cycling program was 38 cycles (94 °C for 1 min and 56 °C for 4 min), and a hold at 4 °C. A total of 0.5 μL of ligation product was loaded into an ABI3730XL, and the raw data were analyzed using the GeneMapper 4.1 software program . All of the iMLDR primers are presented in The genomic DNA was isolated from ear tissue using the standard phenol-chloroform protocol method . The DNAFABP3 (rs1110770079) and PHKG1 (rs697732005) were assessed using the PCR-RFLP method due to the failure of the iMLDR technique. For PCR-RFLP, the sequences for these 2 genes were obtained from the GenBank database . The primers of these 2 SNPs were designed by Primer Premier 6.0 software   . PCR reaStatistical analysis of IFC and ABT of the 330 Suhuai pigs was performed using GraphPad Prism 6  with the quartile method.Phenotypic correlations of IFC and ABT were calculated using SAS 9.2 software . To estimate genetic correlations of IFC and ABT, all the phenotypes were corrected for sex and batch as fixed effects and sample identity (ID) as a random variable using DMU software .In this study, genetic diversity indices were calculated to explore the genetic structure of all SNPs. The minimal number of genotypes used in the analysis was 3, and the total number of animals analyzed here was 327–330. The genotype and allele frequencies were calculated using Microsoft Excel 2013. The heterozygosity (He), homozygosity (Ho) and polymorphic information content (PIC) were estimated using Power Marker V3.0 software [Here, variance analyses were performed using SAS 9.2 software . Sex and batch were considered as fixed effects in the models (1) and (2), because sex and batch had significantly effects on both IFC and ABT in variance analyses. We also found that age had significant effects on IFC, and carcass weight had significantly effects on ABT. Thus, age was used as the covariate for IFC in model (1), and carcass weight was used as the covariate for ABT in model (2).Association analysis of each SNP with IFC and ABT was performed using the PROC GLM procedure of module of SAS 9.2 software. For IFC, the following model was applied:For ABT, the following model was applied:ijlkm represents the vector of the phenotypic value of the trait under study, μ in these 2 models is the population mean of IFC and ABT, respectively, Gi, Bj and Sk refer to fixed effects of SNPs, batch and sex, respectively, Dl represents the covariate of age for IFC, Wl is the covariate of carcass weight for ABT, Km is the random variable of kinship matrix using pedigree, and eijklm represents the random error. All statistical analyses were considered significant at p < 0.05.In models (1) and (2), YDescriptive statistics for IFC and ABT of 330 Suhuai pigs are shown in p < 0.01) and genetic correlation was 0.34 (p < 0.01). These results suggested a moderate correlation between IFC and ABT of Suhuai pigs [The phenotypic and genetic correlations between IFC and ABT were calculated. Phenotypic correlation between IFC and ABT was 0.32 (LIPE (rs328830166), IGF2 (g.3072G > A), MC4R (rs81219178), SCD (rs80912566), UBE3C (rs81329544), FABP3 (rs1110770079), PHKG1 (rs697732005). The values of He, Ho and PIC of the LEP , LEPR , RETN  and RYR1  were low, due to the lack of homozygous mutant genotypes. Moreover, the value of PIC in the IGF1 (0.09) SNP was low due to the absence of one homozygous genotype.The genotypic and allele frequencies, as well as other genetic parameters , were estimated for the Suhuai pigs . Of thesFABP3 (rs1110770079) SNP was associated with IFC (p < 0.05). Among the three genotypes of the FABP3 gene (rs1110770079), IFC of the GG genotype (2.19 ± 0.09 %) was significantly higher than that of the TT genotype (1.93 ± 0.07 %). In addition, the results revealed a tendency (p < 0.1) for differences among the RYR1 (rs344435545) and SCD (rs80912566) genotypes for IFC. However, none of these SNPs was associated with ABT. Association analysis results of SNPs with IFC and ABT are shown in CTN1 gene is associated with IFC [IFC and ABT are important economic traits for meat quality. Research on the genetic basis of these two traits has long been the focus of attention ,5,6. Recwith IFC . IFC is with IFC , but higwith IFC . Therefowith IFC  reportedIGF1 (rs322131043), LEP (rs45431504), LEPR (rs45435518), RETN (rs327132149) and RYR1 (rs344435545) were observed. These results indicated that there was a strong selection pressure on several traits that were associated with SNPs in this study, such as growth rate, yield of lean meat, BF thickness and so on. The lower values of PIC and He in the IGF1 (rs322131043), LEP (rs45431504), LEPR (rs45435518) and RETR (rs327132149) provided additional evidence for the strong selection pressure. For example, the RYR1 (rs344435545) SNP is a missense mutation that causes a change in protein (Arg615→Cys615), and this mutation results in muscle dysfunction and finally porcine stress syndrome (PSS) [In the present study, 13 SNPs from 12 previously reported candidate genes for IFC were genotyped, and these SNPs were polymorphic in Suhuai pigs. Among these SNPs, low polymorphisms of the alleles me (PSS) . This diIGF1 rs3131043, LFABP3 (rs1110770079) SNP. These 13 SNPs did not associate with ABT of Suhuai pigs; the possible reasons were that some of these genes were not causal genes for BF thickness, such as PHKG1, RETN, SCD, and UBE3C. The FABP3 gene is one member of the fatty acid binding protein family, which plays a critical role in intracellular fatty acid transport by binding lipids and regulating metabolic homeostasis [FABP3 gene might be responsible for IFC and is often regarded as a candidate gene [FABP3 (HinfI) SNP was associated with IFC in both Yanan (p < 0.001) and DLY (p < 0.05) pigs, but did not significantly affect the BF thickness, strikingly similar to the results in our study. Although the rs344435545 SNP in the RYR1 gene is likely to cause an increase in the incidence of porcine stress syndrome, there is a strong correlation between IFC and RYR1 gene expression level [SCD (rs80912566) SNP could affect the fatty acid composition and IFC within the Duroc population [SCD gene was identified as a candidate gene related to IFC between pigs with high and low IFC [SCD (rs80912566) SNP was associated with fat composition but not with fat content. Their results were consistent with our study. It is worth noting that the results in p < 0.1) of association between the RYR1 (rs344435545) and SCD (rs80912566) with IFC. We speculate that these two SNPs have small effects on IFC or these may be only in linkage disequilibrium with the causative mutation of IFC.Association analysis of the 13 evaluated SNPs with IFC and ABT was performed. Associations of all SNPs with IFC were not significant, except for the eostasis . The FABate gene  found thon level  and as tpulation . MoreoveIGF1 gene affects the regulation of adipogenesis. Several results suggested a degree of positive correlation between the IGF1 gene expression and adipocyte content [IGF1 gene, the LEP and LEPR genes also play a role in adipogenesis, and several studies have found that LEP (rs45431504) and LEPR (rs45435518) are polymorphic and could significantly influence IFC and BF thickness [RETN gene is located on SSC2, and RETN (rs327132149) was significantly associated with the abdominal fat weight, BF thickness and IFC [IGF1 (rs322131043), LEP (rs45431504), LEPR (rs45435518) and RETN (rs327132149) were not significantly associated with IFC and ABT in our study and might be strongly selected for these 3 genes in Suhuai population, resulting in low polymorphisms in these SNPs.The  content , and withickness ,47,48. TN rs3271349 was siLIPE gene has long been considered as a candidate gene that could affect IFC deposition due to resolving fat [IGF2 (g.3072G > A) could affect pig carcass traits and IFC in Large White×Landrace populations. On the contrary, Aslan et al. [IGF2 (g.3072G > A) SNP may not affect IFC in Pietrain, Duroc and Large White populations. Supakankul et al. [UBE3C polymorphisms (rs81329544 and rs32466023) were associated with IFC and fatty acid composition. The UBE3C gene is considered a potential candidate gene for fat deposition in muscle because of its location on SSC18, near the QTLs for IFC and FA composition [PHKG1 gene is related to glycolysis potential and could affect pork quality. A previous study indicated that there was a point mutation (rs330928088) in a splice acceptor site of intron 9 in the PHKG1 gene. This point mutation gave rise to the 32 bp deletion in the open reading frame (ORF) and generated a premature stop codon [PHKG1 (rs330928088) SNP is not polymorphic and did not cause the 32 bp deletion of Suhuai pigs. The SNP (rs697732005) in front of this PHKG1 (rs330928088) SNP is polymorphic, although it was not associated with IFC in this study. The MC4R gene has only one exon, located in the QTL for ABT on SSC1, and plays an important role in the regulation of energy homeostasis [MC4R gene could be associated with fat mass in humans [MC4R (rs81219178) SNP, similar to our results. Nevertheless, there was no significant difference in IFC between the three genotypes of the MC4R (rs81219178) in pigs [The ving fat ,50. Burgving fat  reportedn et al.  found thl et al.  reportedposition . The PHKop codon . Howevereostasis . Severaln humans . Lyadskin humans  found noC4R rs812178 SNP, FABP3 (rs1110770079) SNP was the genetic marker we were looking for, which could improve IFC without increasing BF thickness.These 13 SNPs of the 12 genes did not associate with ABT in Suhuai pigs. The possible reasons were that some of these genes were not causal genes for BF thickness, and other candidate genes of BF thickness may have heterogeneity between breeds. Finally, the FABP3 (rs1110770079) SNP was associated with IFC (p < 0.05) but not with ABT, and this confirmed the importance of porcine FABP3 as a candidate gene for IFC of Suhuai pigs. The cost of genotyping tests is low, and farmers could select favorable genotype (GG) individuals according to the genotyping results for pig breeding. Further studies are necessary to confirm the finding.Our results showed that phenotypic and genetic correlations between IFC and ABT were moderate. These 13 SNPs of the 12 genes were polymorphic in Suhuai pigs. Among them,


Rapid detection of allelic variation and identification of advantage haplotypes responsible for spike related traits play a crucial role in wheat yield improvement. The released genome sequence of hexaploid wheat (Chinese Spring) provides an extraordinary opportunity for rapid detection of natural variation and promotes breeding application. Here, selection signals detection and genome-wide association study (GWAS) were conducted for spike related traits. Based on the genotyping results by 90K SNP chip, 192 common wheat samples from southwest China were analyzed. One hundred and forty-six selective windows and one hundred and eighty-four significant SNPs  were detected. Furthermore, tightly linkage and environmental stability window clusters and SNP clusters were also obtained. As a result, four SNP clusters associated with spike length were detected on chromosome 2A, 2B, 2D, and 6A. Two SNP clusters correlated to kernels per spike were detected on 2A and 2B. One pleiotropy SNP cluster correlated to spikelet number and kernels per spike was detected on 7B. According to the genome sequence, these SNP clusters and their overlapped/flanking QTLs which have been reported previously were integrated to a physical map. The candidate genes responsible for spike length, kernels per spike and spikelet number were predicted. Based on the genotypes of cultivars in south China, two advantage haplotypes associated with spike length and one advantage haplotype associated with kernels per spike/spikelet number were detected which have not been effectively transited into cultivars. According to these haplotypes, KASP markers were developed and diagnosed across landraces and cultivars which were selected from south and north China. Consequently, KASP assay, consistent with the GWAS results, provides reliable haplotypes for MAS in wheat yield improvement. Triticum aestivum L.) is the most widely grown food crop and provides the main energy requirements for about one third of the global people  is mainly based on phenotypic selection which is one of the most important steps for genetic improvement. However, it is blindness, empirical, inefficiency and costs a long time. Fortunately, the hardworking and intelligence of the breeders would have kept signatures in the wheat genome during crop improvement, and this selection signal could be detected using different methods  opened a new avenue in exploring genome sequences, isolating novel genes and rapidly detecting natural variations. Consideration of the forward genetics, IWGSC RefSeq v1.0 with precise scaffold ordering and annotation, integrated assembly and complete gene models, could fully resolve problems  gene locates on the long arm of chromosome 2D near the centromere and affects spike compactness, grain size, grain shape and grain number per spike  mapped on the short arm of chromosome 1A and productive tiller number gene (PTN) were notedly associated with spike number per plant . Outliers were deleted before analysis. Analysis of variance (ANOVA) was performed to test the differences caused by the influence of genotype and environment for each spike trait. Pearson's correlation analyses were conducted to pinpoint the relationships among the spike related traits.Main spikes of six plants were randomly selected for phenotype analysis. Spike length was measured from the base of the rachis to the topmost spikelet, excluding the awns. Spikelet number was counted from the basal sterile spikelet to the top fertile spikelet. Kernels per spike were estimated by hand-threshing the maturity spike. Thousand kernel weight was measured by SC-E software  from weighting more than 200 random kernels with two technical repeats. Spike number per plant was counted the spike with more than five kernels in one plant. Statistical data were analyzed using SPSS 19.0 software (Genomic DNA was extracted from the fresh leaves of seedling wheat using the modified cetyl trimethyl ammonium bromide (CTAB) method  values were calculated using SNPs within 200 Mb for cultivars, landraces and all samples, respectively. The distance that the LD decays to half of its maximum value was estimated.Based on the pruned data, linkage disequilibrium (LD) was calculated using PLINK60 (Version 1.90) software and the LD decay graphs were plotted using an R script. The pairwise FST) and reduction of nucleotide diversity (ROD = 1–πcultivar/πlandrance) were calculated for non-overlapping 100-kb sliding windows across the genome using VCFtools v0.1.14 (https://github.com/vcftools/vcftools). The windows in the upper 99% of the pool's empirical distribution for both FST statistics and ROD values were selected as candidate regions. These genes, located in the windows detected by two methods or existed in the window clusters according to the distance less than 1 Mb based on the LD, were chosen as candidate genes. The annotation information of these candidate genes were extracted from the IWGSC website (http://www.wheatgenome.org/). Meanwhile, Arabidopsis (https://www.arabidopsis.org/) and rice (http://rapdb.dna.affrc.go.jp/) functional gene databases were used for annotation.The genetic differentiation  was used to declare significant SNPs for GWAS results. To uncover the candidate clusters, stable SNPs (based on LD decay) existed in more than two environments underlying association signals were selected.GWAS for spike related traits were performed in 192 wheat lines using the compressed mixed linear model (CMLM) by the GAPIT package, which took the results of population stratification and kinship as covariate to minimize false positives . MapChart Ver. 2.3 was used for map drawing (https://www.wageningenur.nl/en/show/Mapchart.htm).  were positioned onto the newly released reference genome sequence of Chinese Spring by blasting their flanking or peaking marker sequences against the IWGSC RefSeq v1.0  and 130 cultivars (67 southern lines and 73 northern lines), collected from diverse wheat zoning in China  and polymorphism levels (ROD) between cultivars and landraces were calculated  was covered by the reported QTL qSL-2B.7  was covered by the reported QTLs qKPS-7B.5 and qKPS-7B.6 . Meanwhile, they were physically closed to the SNP clusters, qSL-2D and qTKW-2D.The SNP clusters mentioned above  and spike related QTLs reported previously were integrated on the physical maps Figures . The res7 Figure . qKPS/SN6 Figure . qSL-2A Arabidopsis functional gene databases were used. The result revealed that some of these genes were involved in selection-related agronomic traits such as stress response, development  . Based on the LD decay, the candidate region contained only 55 candidate genes , but it was also difficult to detect the candidate genes. By contrast, the SNP cluster qKPS-2A covered region (0.01 Mb) together with the LD region only including 33 candidate genes  during selection (Michel et al., In summary, seven SNP clusters involved in wheat spike traits were detected by selection signal analysis and GWAS. Based on the released wheat genome sequence, an integrated map which contains the SNP clusters and their overlapped/flanking QTLs was constructed. KASP markers to identify two advantage haplotypes were developed for increasing SL, KPS and SN in further breeding program.JL, BF, and ZX designed the research. JL, ZX, BF, XF, QZ, JC, FW, GJ, and LY conducted phenotype of the wheat population. JL analyzed the data and wrote the manuscript; Funding was acquired by TW. BF and TW had primary responsibility for final content. All authors contributed to manuscript revision, read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure . At this gene, the P-values provided by other six tests all reached the suggestive significance level (p < 5 × 10−5). Regarding the computation time required for a genome-wide G × E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G × E analyses.The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The “adaptive combination of Bayes factors method” (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the “Set-Based gene-EnviRonment InterAction test” (SBERIA), “gene-environment set association test” (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The  However, compared with the success achieved in identifying genetic main effects, very few G × E findings have been replicated partially due to the lack of power  analysis methods have been developed to aggregate the G × E signals within a gene/region and alleviate the multiple-testing penalty , which is a variance component (VC) test that estimates the SNP main effects using a ridge regression  or random (designated “INT_RAN”). They also developed a joint test for detecting the genetic associations while allowing for G × E (designated “JOINT”)  has been proposed as a powerful polygenic approach to detect G × E  at the lth SNP , E be the environmental factor, and X be the vector of potential confounder covariates. First, we assess each SNP × E interaction by considering the following generalized linear model (GLM):Suppose a gene or an analysis region contains i” that represents the data of the ith subject. The SNP × E interaction is of interest, and therefore H0:βGE = 0 vs. H1:βGE ≠ 0. Let GE. According to the asymptotic normality of MLE, GE and a variance of V, i.e., For simplicity, we omit the subscript “W, i.e., βGE ~ N. The Bayes factor (BF)  will be between 0 and 2, in the same range as Gl.where G ~ N, where W = 0.22 = 0.04. This prior implies that we believe 95% of odds ratios (ORs) range from exp(−2 × 0.2) = 0.67 to exp(2 × 0.2) = 1.49. Now that GlE is in the same range as Gl, we consider using the same prior for SNP × E interaction, i.e., βGE ~ N where W = 0.22 = 0.04. Reported SNP × E interactions have been of modest effect sizes that can be positive or negative  may be a reasonable prior for βGE  implies that 95% of βGEls range from (−2 × 0.2) = −0.4 to (2 × 0.2) = 0.4. This may also be a reasonable prior for βGE when traits are continuous.To apply ADABF to continuous traits, we should first standardize the traits to have a mean of 0 and a standard deviation of 1, as implemented in our ADABF R code that can be downloaded from W = 0.22 = 0.04) may be slightly large for βGEls. However, a larger prior variance can just reflect our uncertainty of the prior information  ≥ BF(2) ≥ ⋯ ≥ BFL). We draw B sets of 0 and a variance-covariance matrix incorporating the pairwise linkage disequilibrium (LD) among the L SNPs, and then calculate After calculating the BFs of all the n et al. .Sk with its counterparts from H0  for the observed sample and for each of the resampling replicates. By comparing these minimum P-values, we obtain the significance of G × E for the observed sample. The efficient sequential resampling procedure  and E (coded as 0 or 1) is responsible for the susceptibility of a rare disease. Subjects with E = 1 and SNP = 2 will have an increased disease risk. If cases are ascertained, more E = 1 and SNP = 2 combinations will be observed in cases, representing that SNP and E will be positively associated in cases. Assuming SNP and E are approximately independent in controls, they will be also positively associated in the combined case-control data  at the L SNPs, and ŵ is the vector of weights given to the L SNPs. The weight is determined by the sign and significance of the filtering statistic. The weight given to a SNP is 1 if it is positively associated with E, −1 if it is negatively associated with E, and is a very small value  if the SNP is not statistically associated with E  if the SNP is not statistically associated with Y  and the following interaction test statistic (stage 2) must be asymptotically independent under the null hypothesis. Dai et al. have proven the validity of using the “main-effect filtering” as the screening strategy  follow a distribution with a mean of 0 and a variance of τ2. The null hypothesis H0:δGE = 0 is then reduced to H0:τ2 = 0. The score statistic to test H0:τ2 = 0 vs. H1:τ2 > 0 can be referred to Equation (6) in Lin et al.  are assumed to be random effects that follow a distribution. Therefore, testing H0:δGE = 0 can be reduced to testing H0:τ2 = 0. However, these five VC tests are dissimilar in two aspects.Similar among the five VC tests, δGls . INT_FIX treats δGls as fixed effects, whereas INT_RAN assumes δGls follow a distribution with a mean of 0 and a variance of τ1. GESAT and iSKAT use ridge regression to estimate δGls under the null hypothesis of H0:δGE = 0. JOINT simultaneously tests whether SNP main effects or G × E interaction effects exist, i.e., H0:τ1 = τ2 = 0 vs. H1:τ1 > 0 or τ2 > 0. Therefore, it is not a pure test for detecting G × E.First, they take different approaches to estimate the SNP main effects, δGEls  and searches for the optimal ρ. The other four VC tests all assume that δGEls are independent to each other .Second, iSKAT allows an exchangeable correlation ρ among δTo reflect the real LD structures of the human genome, we used GWAS data from the TWB as our simulation material. The TWB aims to build a research database that integrates the genomic profiles, lifestyle patterns, dietary habits, and environmental exposures of residents aged 30–70 years in Taiwan  sharing coefficients among seemingly unrelated individuals from the whole-genome data. Using PLINK-1.9 . In total, 24,769 autosomal genes were identified. Furthermore, following the conventional gene-based tests  INT_FIX: a G × E test that treats the SNP main effects as fixed effects; (2) INT_RAN: a G × E test that treats the SNP main effects as random effects; and (3) JOINT: a joint test of the genetic main effects and G × E interactions. Both GESAT  at the lth SNP, E is the environmental factor, and e is the random error term following the standard normal distribution. Moreover, we simulated binary traits according towhere where the intercept was In Equations (6) and (7), E was a binary environmental factor taking a value of 0 or 1 each with a probability of 0.5. Because E was randomly sampled, the “SNP-E association filtering”  or (7). We repeated 41 rounds of genome-wide G × E analysis for the 24,769 autosomal genes so that each G × E test was evaluated at least one million times . Following the conventional gene-based tests . Thus, if we obtain a significant test result using the JOINT method, we cannot know whether this significance is contributed by G × E or not. Therefore, the JOINT test should not be used if G × E is of the main interest. It is suitable for detecting genetic main effects while allowing for G × E.We also evaluated the validity of these G × E tests in the presence of genetic main effects. If βGEs in a gene. To investigate the impact of the gene length on power, we randomly drew three genes , respectively, incorporating 20, 50, and 100 SNPs, for simulations. Assuming d SNPs interact with E (d = 1 or 4), the continuous traits of the 16,555 subjects were generated according toThe true number of SNPs interacting with E may not be large in the genome  at the lth SNP that interacts with E , and e is the random error term following the standard normal distribution. Moreover, the binary traits were simulated according towhere βGl|) and SNP × E interaction effects (|βGEl|) were evaluated at three levels: small, medium, and large. For continuous traits, the effect sizes were uniformly drawn from  ,  (medium), and  (large), respectively. For binary traits, the effect sizes were uniformly drawn from  ,  (medium), and  (large), respectively.The magnitudes of SNP main effects (|βd = 1 and 8 for d = 4. Scenarios (1-1), (4-1), and (4-5) are pure interaction models without SNP main effect. Scenarios (1-2), (4-2), and (4-6) include SNP × E interaction effects with SNP main effects in the same direction. Scenarios (1-3), (4-3), and (4-7) include SNP × E interaction effects with SNP main effects in the opposite direction. Scenarios (4-4) and (4-8) include SNP × E interaction effects with 50% SNP main effects in the same direction and 50% in the opposite direction.Table d = 1) for continuous and binary traits, respectively. The results of 4 SNP × E  are shown in Figures GEs was decreasing as the number of SNPs increased. For example, when d = 4, the proportions of non-null βGEs were 4/20, 4/50, and 4/100, respectively.Based on 1,000 replications for each scenario, Figures d = 1, Figures m be the number of SNPs in a gene, where m = 20, 50, or 100 in our power comparison. When d = 1, m − 1 SNPs exhibit no interactions with E. ADABF outperformed the other tests because it excluded SNP × E with smaller BF; thus, ADABF was more robust to the inclusion of many (m − 1) null βGEs.The JOINT test was generally the most powerful test. However, as mentioned above, it is not a pure G × E test. Among the 6 pure G × E tests, ADABF was more powerful under 1 SNP × E . The weight (elements in ŵ) given to a SNP is 1 if it is positively associated with Y, −1 if it is negatively associated with Y, and is a very small value  if the SNP is not statistically associated with Y. When 50% SNP main effects were in the same direction with the SNP × E interaction effects while 50% were in the opposite direction, the positive and negative SNP × E interactions in EG′ŵ were canceled out. Therefore, SBERIA suffered from a power loss in Scenarios (4-4) and (4-8).Among the 6 pure G × E tests, SBERIA can be more powerful than ADABF under 4 SNP × E  was binary here. Both DBP and SBP were measured twice in a sitting position, with a 5-min interval between the two measurements. As suggested by Jamieson et al.  and othePrior to the G × E analysis, we first regressed DBP (the average of two measured DBPs) and SBP (the average of two measured SBPs) on gender, age, alcohol drinking, body mass index (BMI), and the first seven PCs. In Table Y be DBP or SBP, Gl be the number of minor allele  at the lth SNP, E be the environmental factor , and X be the vector of covariates, including age, gender, BMI, and the first seven PCs. We fitted a linear regression for each of the 603,741 SNPs,The first strategy to detect G × E is single SNP analysis. Let GC = 1.081 for DBP and 1.058 for SBP. As suggested by GWAS such as Li et al.  . When the P-value of testing H0:γG = 0 vs. H1:γG ≠ 0 was larger than 0.1, the weight given to the lth SNP was 0.0001 : In the filtering stage, a linear regression was fitted for each SNP, i.e., i et al. . Using tE = “alcohol drinking” was binary. The validity of using this filtering stage was justified by Proposition 3 of Dai et al. . When the P-value of testing H0:δG = 0 vs. H1:δG ≠ 0 was larger than 0.1, the weight given to the lth SNP was 0.0001 : In the filtering stage, a logistic regression was fitted for each SNP, i.e., i et al. . AccordiP < 2.5 × 10−6, where ADAMTS7P1 gene was identified by the GESAT test (P = 9.5 × 10−7). At this gene, the P-values provided by other 6 tests all reached the suggestive significance level , the main effects of these SNPs are not significant , is commonly used to weight the SNPs  from Equation (10), the main effects of all the four SNPs within ADAMTS7P1 were not significant. Although the ADAMTS7P1-alcohol interaction effect on SBP did not achieve the suggestive significance level ; ADABF removes the SNP × E with smaller BFs. Both approaches take the advantage of screening out noises, and therefore they are usually more powerful than other pure G × E tests (Figures In this study, we extend our ADABF to G × E detection and compare it with six existing tests. Their validity, power, robustness, and computation time are investigated. SBERIA builds a G × E term by incorporating the SNPs that pass the filtering stage (i.e.,  Figures –6. HowevP < 0.05 or 0.01 is sufficient to reject H0 of no polygenic G × E interactions (Pan et al., P<To detect G × E on a genome-wide scale, ADABF polygenic test (Lin et al., W-YL developed the ADABF method and the analysis tool, designed and performed the simulation study, analyzed the TWB data, and wrote the manuscript. C-CH contributed to the review and coding for the competitor methods. Y-LL, S-JT, and P-HK contributed to the writing of the manuscript, and provided the TWB data. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Kabuli” breeding lines and Iranian landrace “Desi” chickpea genotypes, were genotyped using DArTseq-Based single nucleotide polymorphism (SNP) markers. Out of 3339 SNPs, 1152 markers with known chromosomal position were selected for genome diversity analysis. The number of mapped SNP markers varied from 52 (LG8) to 378 (LG4), with an average of 144 SNPs per linkage group. The chromosome size that was covered by SNPs varied from 16,236.36 kbp (LG8) to 67,923.99 kbp (LG5), while LG4 showed a higher number of SNPs, with an average of 6.56 SNPs per Mbp. Polymorphism information content (PIC) value of SNP markers ranged from 0.05 to 0.50, with an average of 0.32, while the markers on LG4, LG6, and LG8 showed higher mean PIC value than average. Unweighted neighbor joining cluster analysis and Bayesian-based model population structure grouped chickpea genotypes into four distinct clusters. Principal component analysis (PCoA) and discriminant analysis of principal component (DAPC) results were consistent with that of the cluster and population structure analysis. Linkage disequilibrium (LD) was extensive and LD decay in chickpea germplasm was relatively low. A few markers showed r2 ≥ 0.8, while 2961 pairs of markers showed complete LD (r2 = 1), and a huge LD block was observed on LG4. High genetic diversity and low kinship value between pairs of genotypes suggest the presence of a high genetic diversity among the studied chickpea genotypes. This study also demonstrates the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in chickpea. The genotypic markers provided in this study are useful for various association mapping studies when combined with phenotypic data of different traits, such as seed yield, abiotic, and biotic stresses, and therefore can be efficiently used in breeding programs to improve chickpea.Characterization of genetic diversity, population structure, and linkage disequilibrium is a prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes, including advanced “ Cicer arietinum L.) is an important legume food crop that is currently cultivated in wide ranges of climatic regions across the world in more than 45 countries [Desi and Kabuli cultivars, that are distinctly different in agro-morphological characteristics such as seed shape, flower color, growth habit, and genome composition [Desi type is mainly cultivated in Ethiopia and Indian subcontinent [Chickpea  markers have been developed and increasingly utilized as highly-preferred molecular markers in various crop species because of their wide genome coverage, co-dominant inheritance, chromosome-specific location, low cost, and fast tracking in comparison to other polymerase chain reaction (PCR)-based molecular markers ,19. SNP Kabuli breeding lines and landrace Desi accessions.In this study, we used DArTseq-based SNP markers for genetic diversity, population structure, and linkage disequilibrium analysis in 186 chickpea genotypes comprised of advanced Desi accessions and 166 Kabuli advanced breeding lines supplied by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the International Center for Agriculture Research in the Dry Areas (ICARDA), were employed for genetic diversity, linkage disequilibrium, and population structure analyses using DArTseq-based SNP markers. Fresh leaves of each genotype (pooled sample of ten plants per genotypes) were used for DNA extraction using the cetyltrimethyl ammonium bromide (CTAB) method [The 186 chickpea genotypes , includi) method , with miAll chickpea genotypes were genotyped using sequencing-based DArT genotyping platform . This method is based on methyl filtration and next-generation sequencing platforms, as described before ,29. Initwww.xlstat.com). Functional annotation and prediction of effects on SNP markers were calculated in SnpEff v 4.2 [Polymorphism information content (PIC) values for SNP markers were calculated using PowerMarker v.3.25 . Geneticff v 4.2 . PredictIn order to find the fitting grouping of chickpea genotypes, the genetic structure based on the Bayesian clustering approach was implemented by STRUCTURE 2.1 based on an admixture model , as in mIn order to finding the fitting pupation structure, we complemented the STRUCTURE analysis with the discriminant analysis of principal components (DAPC) analysis using the procedure proposed by the R’s package “adegenet” ,39. LinkThe 186 chickpea genotypes were analyzed by DArTseq-SNPs. The initial data set consisted of 3339 SNPs and after filtering data for some quality parameters, including minor allele frequency lower than 0.1, missing data ≥10%, and also SNPs with unknown chromosome position, a total of 1152 SNPs were selected for genome diversity analysis . The numDistribution of heterozygous chickpea genotypes and SNP markers revealed low values of heterozygosity (>0.2) in more than 75% of chickpea genotypes and SNP markers . These oDesi landraces and two Iranian improved Desi cultivar (Kaka and Pirouz). Clusters III and IV were larger groups of chickpea genotypes compared to cluster I and II. Most of the genotypes that originated from ICRISAT grouped in cluster IV. There were no strong relationships between cluster grouping and pedigree of chickpea genotypes, although most of Desi chickpea landraces grouped in cluster III, and in cluster II and cluster IV most of the genotypes showed at least one or two of the same parents in their pedigrees. Most of the genotypes used in this study have been used as parental lines or have similar genetic backgrounds, so a mixture of pedigree is observed in all clusters.Kinship coefficient between pairs of chickpea genotypes varied from −0.83 to 3 . Overall, 66% of the pairs of 186 chickpea genotypes had kinship values of ≤0.5 . KinshipDesi accessions that collected from North West of Iran.Population structure analysis of the genotypes based on the Bayesian model implemented in STRUCTURE software grouped genotypes at K = 4 . Four suPrincipal component analysis (PCA) based on DArTseq-SNP markers revealed four distinct groups of chickpea genotypes and two principal components, accounting for 75.18% of total variation . Discrimp < 0.001) of LD. Mean r2 value was 0.22, while the critical r2 value was 0.33. The overall LD decay in chickpea germplasm was relatively low and few markers showed r2 ≥ 0.8. Nevertheless 2961 pairs of markers showed complete LD (r2 = 1), although a huge LD block was observed on LG4  intra-chromosomal pairs showed a significant level  in gene regions, of which 545 (47.3%) were in exons. This estimated ratio using DArTseq-based SNP markers in chickpea genes is comparable with previously reported orthologous chickpea genes/transcripts .Chickpea is a self-pollinated crop, and it is assumed that all chickpea genotypes in the present study had been held several generations via self-pollination; therefore, they were expected to be mostly homozygous. The low level of heterozygosity in the chickpea panel suggests that the genotypes we used in this study were close to being inbred lines and homozygous. Average inter-chromosomal LD decay of SNP markers showed long distances for marker pairs in LD , which mKinship values calculated between pairs of genotypes are a reliable factor for understanding the extent of relatedness between chickpea genotypes ,50. KinsThe use of the cluster analysis and population structure was in concordance with those of DAPC and showed better performance and allowed for a population subdivision. However, some groups were composed of a mixture of chickpea genotypes, and some genotypes were included in different clusters, which could be due mainly to the same parental lines that were used in the breeding pedigree of these genotypes.This might be due to the fact that these genotypes were utilized in different breeding programs, and thus they have same parental pedigree, as shown in In this study, we employed DArTseq-SNP markers for genome diversity, population structure, and LD analyses in a mini-core collection of advanced chickpea breeding lines and landraces. The large number and high genome coverage of these markers showed high polymorphism in the studied chickpea germplasm, which is a valuable and remarkable tool for genome-wide screening of genetic diversity in chickpea. This genetic diversity of the chickpea gene pool will be highly valuable information for various purposes, including allele mining and donor parent selection for developing new improved chickpea germplasm for target traits of interest. The appropriate DArTseq-SNP markers for different gene/traits of interest can be used for cloning and designing competitive allele specific PCR (KASP) in chickpea.Ascochyta rabiei and Fusarium oxysporum) and seed yield under environmental stresses like drought.Therefore, our next objective is to identify chickpea genotypes with desirable traits, and to conduct association mapping studies focusing on resistance to biotic stresses (


Hypoglycemia is of great concern in older patients, especially when complicated with multimorbidity and geriatric syndromes. We implemented a telemedicine model to address hypoglycemia knowledge, risk factors, incidence and comanagement with their primary care teams (PCT). We identified 166 consecutive rural veterans with high hypoglycemia risk, based on a local medication database (sulfonylureas and insulin), age, and recent glycated hemoglobin A1c (HbA1c). We conducted a telephone medication reconciliation and survey assessing glucose self-monitoring (GSM), hypoglycemia knowledge and symptoms. Variables were tested using chi-square, Fisher’s, and one-way ANOVA. Multivariable logistic regression model was built to assess the association of hypoglycemia and age group, adjusted with treatment, HbA1c%, self-monitoring, and knowledge. There were 54 veterans aged <65 (younger), and 112 veterans aged ≥65 years (older). Average HbA1c was higher in younger than older . There was no difference in treatment regimens, but the older had greater GSM (p=.028) and lower hypoglycemia symptom knowledge (p=.026). Symptomatic hypoglycemia was greater in younger versus older . Recent (past-2-weeks) hypoglycemic events were more frequent in younger than older . Regression analyses showed that younger veterans were more likely to have hypoglycemia . Our results indicate a great need to evaluate older adults with high hypoglycemia risk, in whom we observed less reports of hypoglycemia albeit with similar regimens and lower HbA1c. We suspect greater hypoglycemia unawareness, thus we are implementing a project using continuous glucose monitoring in this high-risk population.