Explore the vast range of titles available.

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

This study aimed to determine the effect of different rates of marker genotyping error on the accuracy of genomic prediction that was examined under distinct marker and quantitative trait loci (QTL) densities and different heritability estimates using a stochastic simulation approach. For each scenario of simulation, a reference population with phenotypic and genotypic records and a validation population with only genotypic records were considered. Marker effects were estimated in the reference population, and then their genotypic records were used to predict genomic breeding values in the validation population. The prediction accuracy was calculated as the correlation between estimated and true breeding values. The prediction bias was examined by computing the regression of true genomic breeding value on estimated genomic breeding value. The accuracy of the genomic evaluation was the highest in a scenario with no marker genotyping error and varied from 0.731 to 0.934. The accuracy of the genomic evaluation was the lowest in a scenario with marker genotyping error equal to 20% and changed from 0.517 to 0.762. The unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value were obtained in the reference and validation populations when the rate of marker genotyping error was equal to zero. The results showed that marker genotyping error can reduce the accuracy of genomic evaluations. Moreover, marker genotyping error can provide biased estimates of genomic breeding values. Therefore, for obtaining accurate results it is recommended to minimize the marker genotyping errors to zero in genomic evaluation programs.

Abstract Comprehensive decisions on the management of commercially produced bees, depend largely on associated knowledge of genetic diversity. In this study, we present novel microsatellite markers to support the breeding, management, and conservation of the blue orchard bee, Osmia lignaria Say (Hymenoptera: Megachilidae). Native to North America, O. lignaria has been trapped from wildlands and propagated on-crop and used to pollinate certain fruit, nut, and berry crops. Harnessing the O. lignaria genome assembly, we identified 59,632 candidate microsatellite loci in silico, of which 22 were tested using molecular techniques. Of the 22 loci, 12 loci were in Hardy-Weinberg equilibrium (HWE), demonstrated no linkage disequilibrium (LD), and achieved low genotyping error in two Intermountain North American wild populations in Idaho and Utah, USA. We found no difference in population genetic diversity between the two populations, but there was evidence for low but significant population differentiation. Also, to determine if these markers amplify in other Osmia, we assessed 23 species across the clades apicata, bicornis, emarginata, and ribifloris. Nine loci amplified in three species/subspecies of apicata, 22 loci amplified in 11 species/subspecies of bicornis, 11 loci amplified in seven species/subspecies of emarginata, and 22 loci amplified in two species/subspecies of ribifloris. Further testing is necessary to determine the capacity of these microsatellite loci to characterize genetic diversity and structure under the assumption of HWE and LD for species beyond O. lignaria. These markers will inform the conservation and commercial use of trapped and managed O. lignaria and other Osmia species for both agricultural and nonagricultural systems.

Specimens from natural history collections (NHCs) are increasingly being used for genetic studies and can provide information on extinct populations, facilitate comparisons of historical and contemporary populations, produce baseline data before environmental changes, and elucidate patterns of change. Destructive sampling for DNA may be in disagreement with NHC goals of long-term care and maintenance. Differentiating quality among sample sources can direct destructive sampling to the source predicted to yield the highest quality DNA and most reliable data, potentially reducing damage to specimens, laboratory costs, and genotyping errors. We used the kit fox (Vulpes macrotis) as a model species and evaluated the quality and reliability of genetic data obtained from carnivoran specimens via three different sample sources: cranial bones, nasal bones, and toepads. We quantified variation in microsatellite amplification success and genotyping error rates and assessed the reliability of source-specific genic data. Toepads had the highest amplification success rates and lowest genotyping error rates. Shorter loci had higher amplification success and lower allelic dropout rates than longer loci. There were substantial differences in the reliability of resulting multilocus genotypes. Toepads produced the most reliable data, required the fewest replicates, and therefore, had the lowest costs to achieve reliable data. Our results demonstrate that the quality of DNA obtained from specimens varies by sample source and can inform NHCs when evaluating requests for destructive sampling. Our results suggest that prior to large-scale specimen sampling, researchers should conduct pilot studies to differentiate among source-specific data reliability, identify high performing loci, reduce costs of analyses, and minimize destructive sampling.

Advances in plant molecular breeding have resulted in the development of new varieties with superior traits, thus improving the crop germplasm. Breeders can screen a large number of accessions without rigorous and time-consuming phenotyping by marker-assisted selection (MAS). Molecular markers are one of the most imperative tools in plant breeding programmes for MAS to develop new cultivars possessing multiple superior traits. Single nucleotide polymorphisms (SNPs) are ideal for MAS due to their low cost, low genotyping error rates, and reproducibility. Kompetitive Allele Specific PCR (KASP) is a globally recognized technology for SNP genotyping. KASP is an allele-specific oligo extension-based PCR assay that uses fluorescence resonance energy transfer (FRET) to detect genetic variations such as SNPs and insertions/deletions (InDels) at a specific locus. Additionally, KASP allows greater flexibility in assay design, which leads to a higher success rate and the capability to genotype a large population. Its versatility and ease of use make it a valuable tool in various fields, including genetics, agriculture, and medical research. KASP has been extensively used in various plant-breeding applications, such as the identification of germplasm resources, quality control (QC) analysis, allele mining, linkage mapping, quantitative trait locus (QTL) mapping, genetic map construction, trait-specific marker development, and MAS. This review provides an overview of the KASP assay and emphasizes its validation in crop improvement related to various biotic and abiotic stress tolerance and quality traits.

Several options are available to the scientific community for genetic map construction but few are simple to install and use. Available programs either lack intuitive interface or are commercial, expensive for many laboratories. We present MapDisto, a free, user-friendly and powerful program for constructing genetic maps from experimental segregating populations. MapDisto is freely available at http://mapdisto.free.fr/DL/ . Current version: 1.7.5.

Genotypic data has been applied in multiple research fields, such as molecular biology, genetics, and breeding. However, due to various reasons, genotyping data inevitably contains a certain percentage of errors, which affects the reliability of gene localization. This study conducted quantitative trait locus (QTL) mapping for some yield related and disease resistant traits, using repeated genotyping data of two wheat RIL populations derived from Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64, and non-erroneous data consisting of consistent genotypes between the two replications. Mapping results were compared with reported QTL for the corresponding traits. Then error correction methods implemented in software packages QTL IciMapping (EC), Genotype-Corrector (GC) and R/qtl were applied to these datasets, followed by QTL mapping. Simulation study was performed by randomly adding five levels of genotyping errors to investigate the effect of genotyping errors on QTL mapping and the efficiency of error correction methods. The results indicated that the impact of genotyping errors on QTL mapping was mainly reflected in the decrease in QTL detection power and accuracy of estimation for QTL positions and effects. The impact was increased with the increasing of errors. EC, GC, and R/qtl improved the mapping accuracy, but all of them cannot eliminate the negative effect of errors completely. Considering detection power, false discovery rate, estimation of QTL position and effect, correction accuracy, and running time, EC was better than the other two methods.

Key message The paper proposes and validates a robust method for rapid construction of high-density linkage maps suitable for autotetraploid species. Modern genotyping techniques are producing increasingly high numbers of genetic markers that can be scored in experimental populations of plants and animals. Ordering these markers to form a reliable linkage map is computationally challenging. There is a wide literature on this topic, but most has focussed on populations derived from diploid, homozygous parents. The challenge of ordering markers in an autotetraploid population has received little attention, and there is currently no method that runs sufficiently rapidly to investigate the effects of omitting problematic markers on map order in larger datasets. Here, we have explored the use of multidimensional scaling (MDS) to order markers from a cross between autotetraploid parents, using simulated data with 74–152 markers on a linkage group and also experimental data from a potato population. We compared different functions of the recombination fraction and LOD score to form the MDS stress function and found that an LOD 2 weighting generally performed well, including when missing values and genotyping errors are present. We conclude that an initial analysis using unconstrained MDS gives a rapid method to detect and remove problematic markers, and that a subsequent analysis using either constrained MDS or principal curve analysis gives reliable marker orders. The latter approach is also particularly rapid, taking less than 10 s on a set of 258 markers compared to 6 days for the JoinMap software. This MDS approach could also be applied to experimental populations of diploid species.

Rapid advancements in technologies provide various tools to analyze fruit crop genomes to better understand genetic diversity and relationships and aid in breeding. Genome-wide single nucleotide polymorphism (SNP) genotyping arrays offer highly multiplexed assays at a relatively low cost per data point. We report the development and validation of 1.4M SNP Axiom® Citrus HD Genotyping Array (Citrus 15AX 1 and Citrus 15AX 2) and 58K SNP Axiom® Citrus Genotyping Arrays for Citrus and close relatives. SNPs represented were chosen from a citrus variant discovery panel consisting of 41 diverse whole-genome re-sequenced accessions of Citrus and close relatives, including eight progenitor citrus species. SNPs chosen mainly target putative genic regions of the genome and are accurately called in both Citrus and its closely related genera while providing good coverage of the nuclear and chloroplast genomes. Reproducibility of the arrays was nearly 100%, with a large majority of the SNPs classified as the most stringent class of markers, “PolyHighResolution” (PHR) polymorphisms. Concordance between SNP calls in sequence data and array data average 98%. Phylogenies generated with array data were similar to those with comparable sequence data and little affected by 3 to 5% genotyping error. Both arrays are publicly available.

Background Scrapie is an infectious prion disease in sheep. Selective breeding for resistant genotypes of the prion protein gene ( PRNP ) is an effective way to prevent scrapie outbreaks. Genotyping all selection candidates in a population is expensive but existing pedigree records can help infer the probabilities of genotypes in relatives of genotyped animals. Results We used linear models to predict allele content for the various PRNP alleles found in Icelandic sheep and compiled the available estimates of relative scrapie susceptibility (RSS) associated with PRNP genotypes from the literature. Using the predicted allele content and the genotypic RSS we calculated estimated breeding values (EBV) for RSS. We tested the predictions on simulated data under different scenarios that varied in the proportion of genotyped sheep, genotyping strategy, pedigree recording accuracy, genotyping error rates and assumed heritability of allele content. Prediction of allele content for rare alleles was less successful than for alleles with moderate frequencies. The accuracy of allele content and RSS EBV predictions was not affected by the assumed heritability, but the dispersion of prediction was affected. In a scenario where 40% of rams were genotyped and no errors in genotyping or recorded pedigree, the accuracy of RSS EBV for ungenotyped selection candidates was 0.49. If only 20% of rams were genotyped, or rams and ewes were genotyped randomly, or there were 10% pedigree errors, or there were 2% genotyping errors, the accuracy decreased by 0.07, 0.08, 0.03 and 0.04, respectively. With empirical data, the accuracy of RSS EBV for ungenotyped sheep was 0.46–0.65. Conclusions A linear model for predicting allele content for the PRNP gene, combined with estimates of relative susceptibility associated with PRNP genotypes, can provide RSS EBV for scrapie resistance for ungenotyped selection candidates with accuracy up to 0.65. These RSS EBV can complement selection strategies based on PRNP genotypes, especially in populations where resistant genotypes are rare.