Quality Control of Genotypes Using Heritability Estimates of Gene Content at the Marker

Quality control filtering of single-nucleotide polymorphisms (SNPs) is a key step when analyzing genomic data. Here we present a practical method to identify low-quality SNPs, meaning markers whose genotypes are wrongly assigned for a large proportion of individuals, by estimating the heritability of gene content at each marker, where gene content is the number of copies of a particular reference allele in a genotype of an animal (0, 1, or 2). If there is no mutation at the marker, gene content has an additive heritability of 1 by construction. The method uses restricted maximum likelihood (REML) to estimate heritability of gene content at each SNP and also builds a likelihood-ratio test statistic to test for zero error variance in genotyping. As a by-product, estimates of the allele frequencies of markers at the base population are obtained. Using simulated data with 10% permutation error (4% actual error) in genotyping, the method had a specificity of 0.96 (4% of correct markers are rejected) and a sensitivity of 0.99 (1% of wrong markers are accepted) if markers with heritability lower than 0.975 are discarded. Checking of Mendelian errors resulted in a lower sensitivity (0.84) for the same simulation. The proposed method is further illustrated with a real data set with genotypes from 3534 animals genotyped for 50,433 markers from the Illumina PorcineSNP60 chip and a pedigree of 6473 individuals; those markers underwent very little quality control. A total of 4099 markers with P-values lower than 0.01 were discarded based on our method, with associated estimates of heritability as low as 0.12. Contrary to other techniques, our method uses all information in the population simultaneously, can be used in any population with markers and pedigree recordings, and is simple to implement using standard software for REML estimation. Scripts for its use are provided.     

Use of partial least squares regression to predict single nucleotide polymorphism marker genotypes when some animals are genotyped with a low-density panel

High-density single nucleotide polymorphism (SNP) platforms are currently used in genomic selection (GS) programs to enhance the selection response. However, the genotyping of a large number of animals with high-throughput platforms is rather expensive and may represent a constraint for a large-scale implementation of GS. The use of low-density marker (LDM) platforms could overcome this problem, but different SNP chips may be required for each trait and/or breed. In this study, a strategy of imputation independent from trait and breed is proposed. A simulated population of 5865 individuals with a genome of 6000 SNP equally distributed on six chromosomes was considered. First, reference and prediction populations were generated by mimicking high- and low-density SNP platforms, respectively. Then, the partial least squares regression (PLSR) technique was applied to reconstruct the missing SNP in the low-density chip. The proportion of SNP correctly reconstructed by the PLSR method ranged from 0.78 to 0.97 when 90% and 50%, respectively, of genotypes were predicted. Moreover, data sets consisting of a mixture of actual and PLSR-predicted SNP or only actual SNP were used to predict genomic breeding values (GEBVs). Correlations between GEBV and true breeding values varied from 0.74 to 0.76, respectively. The results of the study indicate that the PLSR technique can be considered a reliable computational strategy for predicting SNP genotypes in an LDM platform with reasonable accuracy.     

Accuracy of genotype imputation in sheep breeds

Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re‐sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re‐sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population‐wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re‐sequence data from the 50K panel, the accuracy of imputation was much higher (86–96%). This is promising, suggesting that in silico genome re‐sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed.     

The impact of missing and erroneous genotypes on tagging SNP selection and power of subsequent association tests

OBJECTIVE: Single nucleotide polymorphisms (SNPs) serve as effective markers for localizing disease susceptibility genes, but current genotyping technologies are inadequate for genotyping all available SNP markers in a typical linkage/association study. Much attention has recently been paid to methods for selecting the minimal informative subset of SNPs in identifying haplotypes, but there has been little investigation of the effect of missing or erroneous genotypes on the performance of these SNP selection algorithms and subsequent association tests using the selected tagging SNPs. The purpose of this study is to explore the effect of missing genotype or genotyping error on tagging SNP selection and subsequent single marker and haplotype association tests using the selected tagging SNPs. METHODS: Through two sets of simulations, we evaluated the performance of three tagging SNP selection programs in the presence of missing or erroneous genotypes: Clayton's diversity based program htstep, Carlson's linkage disequilibrium (LD) based program ldSelect, and Stram's coefficient of determination based program tagsnp.exe. RESULTS: When randomly selected known loci were relabeled as 'missing', we found that the average number of tagging SNPs selected by all three algorithms changed very little and the power of subsequent single marker and haplotype association tests using the selected tagging SNPs remained close to the power of these tests in the absence of missing genotype. When random genotyping errors were introduced, we found that the average number of tagging SNPs selected by all three algorithms increased. In data sets simulated according to the haplotype frequecies in the CYP19 region, Stram's program had larger increase than Carlson's and Clayton's programs. In data sets simulated under the coalescent model, Carlson's program had the largest increase and Clayton's program had the smallest increase. In both sets of simulations, with the presence of genotyping errors, the power of the haplotype tests from all three programs decreased quickly, but there was not much reduction in power of the single marker tests. CONCLUSIONS: Missing genotypes do not seem to have much impact on tagging SNP selection and subsequent single marker and haplotype association tests. In contrast, genotyping errors could have severe impact on tagging SNP selection and haplotype tests, but not on single marker tests.     

High-throughput single nucleotide polymorphism genotyping in wheat (Triticum spp.)

Over the past few years, considerable progress has been made in high-throughput single nucleotide polymorphism (SNP) genotyping technologies, largely through the investment of the human genetics community. These technologies are well adapted to diploid species. For plant breeding purposes, it is important to determine whether these genotyping methods are adapted to polyploidy, as most major crops are former or recent polyploids. To address this problem, we tested the capacity of the multiplex technology SNPlex™ with a set of 47 wheat SNPs to genotype DNAs of 1314 lines that were organized in four 384-well plates. These lines represented different taxa of tetra- and hexaploid Triticum species and their wild diploid relatives. We observed 40 markers which gave less than 20% missing data. Different methods, based on either Sanger sequencing or the MassARRAY^® genotyping technology, were then used to validate the genotypes obtained by SNPlex™ for 11 markers. The concordance of the genotypes obtained by SNPlex™ with the results obtained by the different validation methods was 96%, except for one discarded marker. Furthermore, a mapping study on six markers showed the expected genetic positions previously described. To conclude, this study showed that high-throughput genotyping technologies developed for diploid species can be used successfully in polyploids, although there is a need for manual reading. For the first time in wheat species, a core of 39 SNPs is available that can serve as the basis for the development of a complete SNPlex™ set of 48 markers.     

Prioritizing animals for dense genotyping in order to impute missing genotypes of sparsely genotyped animals

Background

Genotyping accounts for a substantial part of the cost of genomic selection (GS). Using both dense and sparse SNP chips, together with imputation of missing genotypes, can reduce these costs. The aim of this study was to identify the set of candidates that are most important for dense genotyping, when they are used to impute the genotypes of sparsely genotyped animals. In a real pig pedigree, the 2500 most recently born pigs of the last generation, i.e. the target animals, were used for sparse genotyping. Their missing genotypes were imputed using either Beagle or LDMIP from T densely genotyped candidates chosen from the whole pedigree. A new optimization method was derived to identify the best animals for dense genotyping, which minimized the conditional genetic variance of the target animals, using either the pedigree-based relationship matrix (MCA), or a genotypic relationship matrix based on sparse marker genotypes (MCG). These, and five other methods for selecting the T animals were compared, using T = 100 or 200 animals, SNP genotypes were obtained assuming Ne =100 or 200, and MAF thresholds set to D = 0.01, 0.05 or 0.10. The performances of the methods were compared using the following criteria: call rate of true genotypes, accuracy of genotype prediction, and accuracy of genomic evaluations using the imputed genotypes.

Results

For all criteria, MCA and MCG performed better than other selection methods, significantly so for all methods other than selection of sires with the largest numbers of offspring. Methods that choose animals that have the closest average relationship or contribution to the target population gave the lowest accuracy of imputation, in some cases worse than random selection, and should be avoided in practice.

Conclusion

Minimization of the conditional variance of the genotypes in target animals provided an effective optimization procedure for prioritizing animals for genotyping or sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/1297-9686-46-46) contains supplementary material, which is available to authorized users.     

Large scale SNP unearthing and genetic architecture analysis in sea-captured and cultured populations of Cynoglossus semilaevis

Knowledge on population structure and genetic diversity is a focal point for association mapping studies and genomic selection. Genotyping by sequencing (GBS) represents an innovative method for large scale SNP detection and genotyping of genetic resources. Here we used the GBS approach for the genome-wide identification of SNPs in a collection of Cynoglossus semilaevis and for the assessment of the level of genetic diversity in C. semilaevis genotypes. GBS analysis generated a total of 55.12 Gb high-quality sequence data, with an average of 0.63 Gb per sample. The total number of SNP markers was 563, 109. In order to explore the genetic diversity of C. semilaevis and to select a minimal core set representing most of the total genetic variation with minimum redundancy, C. semilaevis sequences were analyzed using high quality SNPs. Based on hierarchical clustering, it was possible to divide the collection into 2 clusters. The marine fishing populations were clustered and clearly separated from the cultured populations, and the cultured populations from Hebei was also distinct from the other two local populations. These analyses showed that genotypes were clustered based on species-related features. Differential significant SNPs were also captured and validated by GBS and SNaPshot, with linkage disequilibrium and haplotype analysis, seven SNPs have been confirmed to have obvious differentiation in two populations, which may be used as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations. And SNP markers and information on population structure developed in this study will undoubtedly support genome-wide association mapping studies and marker-assisted selection programs. These differential SNPs could be also employed as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations in future.     

A ddRAD Based Linkage Map of the Cultivated Strawberry,Fragaria xananassa

The cultivated strawberry (Fragaria ×ananassa Duch.) is an allo-octoploid considered difficult to disentangle genetically due to its four relatively similar sub-genomic chromosome sets. This has been alleviated by the recent release of the strawberry IStraw90 whole genome genotyping array. However, array resolution relies on the genotypes used in the array construction and may be of limited general use. SNP detection based on reduced genomic sequencing approaches has the potential of providing better coverage in cases where the studied genotypes are only distantly related from the SNP array’s construction foundation. Here we have used double digest restriction-associated DNA sequencing (ddRAD) to identify SNPs in a 145 seedling F₁ hybrid population raised from the cross between the cultivars Sonata (♀) and Babette (♂). A linkage map containing 907 markers which spanned 1,581.5 cM across 31 linkage groups representing the 28 chromosomes of the species. Comparing the physical span of the SNP markers with the F. vesca genome sequence, the linkage groups resolved covered 79% of the estimated 830 Mb of the F. ×ananassa genome. Here, we have developed the first linkage map for F. ×ananassa using ddRAD and show that this technique and other related techniques are useful tools for linkage map development and downstream genetic studies in the octoploid strawberry.     

SNP high-throughput screening in grapevine using the SNPlex™ genotyping system

Background

Until recently, only a small number of low- and mid-throughput methods have been used for single nucleotide polymorphism (SNP) discovery and genotyping in grapevine (Vitis vinifera L.). However, following completion of the sequence of the highly heterozygous genome of Pinot Noir, it has been possible to identify millions of electronic SNPs (eSNPs) thus providing a valuable source for high-throughput genotyping methods.

Results

Herein we report the first application of the SNPlex? genotyping system in grapevine aiming at the anchoring of an eukaryotic genome. This approach combines robust SNP detection with automated assay readout and data analysis. 813 candidate eSNPs were developed from non-repetitive contigs of the assembled genome of Pinot Noir and tested in 90 progeny of Syrah × Pinot Noir cross. 563 new SNP-based markers were obtained and mapped. The efficiency rate of 69% was enhanced to 80% when multiple displacement amplification (MDA) methods were used for preparation of genomic DNA for the SNPlex assay.

Conclusion

Unlike other SNP genotyping methods used to investigate thousands of SNPs in a few genotypes, or a few SNPs in around a thousand genotypes, the SNPlex genotyping system represents a good compromise to investigate several hundred SNPs in a hundred or more samples simultaneously. Therefore, the use of the SNPlex assay, coupled with whole genome amplification (WGA), is a good solution for future applications in well-equipped laboratories.     

Population structure and genetic diversity of coffee progenies derived from Catuaí and Híbrido de Timor revealed by genome-wide SNP marker

The use of single nucleotide polymorphism (SNP) molecular markers has provided advances in selection methodologies used in breeding programs of different crops, reducing cost and time of cultivar release. Despite the great economic and social importance of Coffea arabica, studies with SNP markers are scarce and a small number of SNP are available for this species, when compared with other crops of agronomic importance. Thus, the objective of this study was to identify and validate SNP molecular markers for the species Coffea arabica and to introduce these markers to genetic breeding by means of an accurate analysis of the diversity and genetic structure of breeding populations of this species. After quality filtering, 11,187 SNP markers were selected from the coffee population obtained from crosses between the genotypes Catuaí and Híbrido de Timor. A great number of markers were distributed in the 11 chromosomes, within transcribed regions, and were used to estimate the genetic dissimilarity among the individuals of the breeding population. Dendrogram analysis and a Bayesian approach demonstrated the formation of two groups and the discrimination of all genotypes evaluated. The expressive number of SNP molecular markers distributed throughout C. arabica genome was efficient to discriminate all the accessions evaluated in the experiment, clustering them according to their genealogies. This work identified mixtures within the progenies. The genotyping data also provided detailed information about the parental genotypes and led to the identification of new candidate parents to be introduced to the breeding program. The study discussed population structure and its consequence in obtaining improved varieties of C. arabica.     

High-throughput genotyping in citrus accessions using an SNP genotyping array

We developed a 384 multiplexed SNP array, named CitSGA-1, for the genotyping of Citrus cultivars, and evaluated the performance and reliability of the genotyping. SNPs were surveyed by direct sequence comparison of the sequence tagged site (STS) fragment amplified from genomic DNA of cultivars representing the genetic diversity of citrus breeding in Japan. Among 1497 SNPs candidates, 384 SNPs for a high-throughput genotyping array were selected based on physical parameters of Illumina’s bead array criteria. The assay using CitSGA-1 was applied to a hybrid population of 88 progeny and 103 citrus accessions for breeding in Japan, which resulted in 73,726 SNP calls. A total of 351 SNPs (91 %) could call different genotypes among the DNA samples, resulting in a success rate for the assay comparable to previously reported rates for other plant species. To confirm the reliability of SNP genotype calls, parentage analysis was applied, and it indicated that the number of reliable SNPs and corresponding STSs were 276 and 213, respectively. The multiplexed SNP genotyping array reported here will be useful for the efficient construction of linkage map, for the detection of markers for marker-assisted breeding, and for the identification of cultivars.     

Potential of genotyping-by-sequencing for genomic selection in livestock populations

Background

Next-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays. The aim of this work was to evaluate the potential of GBS compared to SNP array genotyping for genomic selection in livestock populations.

Methods

The value of GBS was quantified by simulation analyses in which three parameters were varied: (i) genome-wide sequence read depth (x) per individual from 0.01x to 20x or using SNP array genotyping; (ii) number of genotyped markers from 3000 to 300 000; and (iii) size of training and prediction sets from 500 to 50 000 individuals. The latter was achieved by distributing the total available x of 1000x, 5000x, or 10 000x per genotyped locus among the varying number of individuals. With SNP arrays, genotypes were called from sequence data directly. With GBS, genotypes were called from sequence reads that varied between loci and individuals according to a Poisson distribution with mean equal to x. Simulated data were analyzed with ridge regression and the accuracy and bias of genomic predictions and response to selection were quantified under the different scenarios.

Results

Accuracies of genomic predictions using GBS data or SNP array data were comparable when large numbers of markers were used and x per individual was ~1x or higher. The bias of genomic predictions was very high at a very low x. When the total available x was distributed among the training individuals, the accuracy of prediction was maximized when a large number of individuals was used that had GBS data with low x for a large number of markers. Similarly, response to selection was maximized under the same conditions due to increasing both accuracy and selection intensity.

Conclusions

GBS offers great potential for developing genomic selection in livestock populations because it makes it possible to cover large fractions of the genome and to vary the sequence read depth per individual. Thus, the accuracy of predictions is improved by increasing the size of training populations and the intensity of selection is increased by genotyping a larger number of selection candidates.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0102-z) contains supplementary material, which is available to authorized users.     

Microarray genotyping resource to determine population stratification in genetic association studies of complex disease

We have developed a robust microarray genotyping chip that will help advance studies in genetic epidemiology. In population-based genetic association studies of complex disease, there could be hidden genetic substructure in the study populations, resulting in false-positive associations. Such population stratification may confound efforts to identify true associations between genotype/haplotype and phenotype. Methods relying on genotyping additional null single nucleotide polymorphism (SNP) markers have been proposed, such as genomic control (GC) and structured association (SA), to correct association tests for population stratification. If there is an association of a disease with null SNPs, this suggests that there is a population subset with different genetic background plus different disease susceptibility. Genotyping over 100 null SNPs in the large numbers of patient and control DNA samples that are required in genetic association studies can be prohibitively expensive. We have therefore developed and tested a resequencing chip based on arrayed primer extension (APEX) from over 2000 DNA probe features that facilitate multiple interrogations of each SNP, providing a powerful, accurate, and economical means to simultaneously determine the genotypes at 110 null SNP loci in any individual. Based on 1141 known genotypes from other research groups, our GC SNP chip has an accuracy of 98.5%, including non-calls.     

A microarray system for Y chromosomal and mitochondrial single nucleotide polymorphism analysis in chimpanzee populations

Chimpanzee populations are diminishing as a consequence of human activities, and as a result this species is now endangered. In the context of conservation programmes, genetic data can add vital information, for instance on the genetic diversity and structure of threatened populations. Single nucleotide polymorphisms (SNP) are biallelic markers that are widely used in human molecular studies and can be implemented in efficient microarray systems. This technology offers the potential of robust, multiplexed SNP genotyping at low reagent cost in other organisms than humans, but it is not commonly used yet in wild population studies. Here, we describe the characterization of new SNPs in Y-chromosomal intronic regions in chimpanzees and also identify SNPs from mitochondrial genes, with the aim of developing a microarray system that permits the simultaneous study of both paternal and maternal lineages. Our system consists of 42 SNPs for the Y chromosome and 45 SNPs for the mitochondrial genome. We demonstrate the applicability of this microarray in a captive population where genotypes accurately reflected its large pedigree. Two wild-living populations were also analysed and the results show that the microarray will be a useful tool alongside microsatellite markers, since it supplies complementary information about population structure and ecology. SNP genotyping using microarray technology, therefore, is a promising approach and may become an essential tool in conservation genetics to help in the management and study of captive and wild-living populations. Moreover, microarrays that combine SNPs from different genomic regions could replace microsatellite typing in the future.     

Genomic evaluations with many more genotypes

Background

Genomic evaluations in Holstein dairy cattle have quickly become more reliable over the last two years in many countries as more animals have been genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as the 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. The overall cost of selection is reduced by genotyping most animals at less than the highest density and imputing their missing genotypes using haplotypes. Algorithms to combine different densities need to be efficient because numbers of genotyped animals and markers may continue to grow quickly.

Methods

Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage disequilibrium was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations using linear and nonlinear methods were compared.

Results

Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations with 50,000 markers and 1.6% lower with 500,000 markers.

Conclusions

Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can be modified to reflect success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.     

Genomic selection prediction models comparing sequence capture and SNP array genotyping methods

The successful application of genomic selection (GS) approaches is dependent on genetic makers derived from high-throughput and low-cost genotyping methods. Recent GS studies in trees have predominantly relied on SNP arrays as the source of genotyping, though this technology has a high entry cost. The recent development of alternative genotyping platforms, tailored to specific species and with low entry cost, has become possible due to advances in next-generation sequencing and genome complexity reduction methods such as sequence capture. However, the performance of these new platforms in GS models has not yet been evaluated, or compared to models developed from SNP arrays. Here, we evaluate the impact of these genotyping technologies on the development of GS prediction models for a Eucalyptus breeding population composed of 739 trees phenotyped for 13 wood quality and growth traits. Genotyping data obtained with both methods were compared for linkage disequilibrium, minor allele frequency, and missing data. Phenotypic prediction methods RR-BLUP and BayesB were employed, while predictive ability using cross validation was used to evaluate the performance of GS models derived from the different genotyping platforms. Differences in linkage disequilibrium patterns, minor allele frequency, missing data, and marker distribution were detected between sequence capture and SNP arrays. However, RR-BLUP and BayesB GS models resulted in similar predictive abilities. These results demonstrate that both genotyping methods are equivalent for genomic prediction of the traits evaluated. Sequence capture offers an alternative for species where SNP arrays are not available, or for when the initial development cost is too high.     

Detection rates for genotyping errors in SNPs using the trio design

One well-known approach for the analysis of transmission-disequilibrium is the investigation of single nucleotide polymorphisms (SNPs) in trios consisting of an affected child and its parents. Results may be biased by erroneously given genotypes. Various reasons, among them sample swap or wrong pedigree structure, represent a possible source for biased results. As these can be partly ruled out by good study conditions together with checks for correct pedigree structure by a series of independent markers, the remaining main cause for errors is genotyping errors. Some of the errors can be detected by Mendelian checks whilst others are compatible with the pedigree structure. The extent of genotyping errors can be estimated by investigating the rate of detected genotyping errors by Mendelian checks. In many studies only one SNP of a specific genomic region is investigated by TDT which leaves Mendelian checks as the only tool to control genotyping errors. From the rate of detected errors the true error rate can be estimated. Gordon et al. [Hum Hered 1999;49:65-70] considered the case of genotyping errors that occur randomly and independently with some fixed probability for the wrong ascertainment of an allele. In practice, instead of single alleles, SNP genotypes are determined. Therefore, we study the proportion of detected errors (detection rate) based on genotypes. In contrast to Gordon et al., who reported detection rates between 25 and 30%, we obtain higher detection rates ranging from 39 up to 61% considering likely error structures in the data. We conclude that detection rates are probably substantially higher than those reported by Gordon et al.     

MPAgenomics: an R package for multi-patient analysis of genomic markers

Background

Last generations of Single Nucleotide Polymorphism (SNP) arrays allow to study copy-number variations in addition to genotyping measures.

Results

MPAgenomics, standing for multi-patient analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation and (ii) selection of genomic markers from multi-patient copy number and SNP data profiles. It provides wrappers from commonly used packages to streamline their repeated (sometimes difficult) manipulation, offering an easy-to-use pipeline for beginners in R.The segmentation of successive multiple profiles (finding losses and gains) is performed with an automatic choice of parameters involved in the wrapped packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given outcome.

Conclusions

MPAgenomics provides an easy tool to analyze data from SNP arrays in R. The R-package MPAgenomics is available on CRAN.     

Sequence-based genotyping for marker discovery and co-dominant scoring in germplasm and populations

Conventional marker-based genotyping platforms are widely available, but not without their limitations. In this context, we developed Sequence-Based Genotyping (SBG), a technology for simultaneous marker discovery and co-dominant scoring, using next-generation sequencing. SBG offers users several advantages including a generic sample preparation method, a highly robust genome complexity reduction strategy to facilitate de novo marker discovery across entire genomes, and a uniform bioinformatics workflow strategy to achieve genotyping goals tailored to individual species, regardless of the availability of a reference sequence. The most distinguishing features of this technology are the ability to genotype any population structure, regardless whether parental data is included, and the ability to co-dominantly score SNP markers segregating in populations. To demonstrate the capabilities of SBG, we performed marker discovery and genotyping in Arabidopsis thaliana and lettuce, two plant species of diverse genetic complexity and backgrounds. Initially we obtained 1,409 SNPs for arabidopsis, and 5,583 SNPs for lettuce. Further filtering of the SNP dataset produced over 1,000 high quality SNP markers for each species. We obtained a genotyping rate of 201.2 genotypes/SNP and 58.3 genotypes/SNP for arabidopsis (n?=?222 samples) and lettuce (n?=?87 samples), respectively. Linkage mapping using these SNPs resulted in stable map configurations. We have therefore shown that the SBG approach presented provides users with the utmost flexibility in garnering high quality markers that can be directly used for genotyping and downstream applications. Until advances and costs will allow for routine whole-genome sequencing of populations, we expect that sequence-based genotyping technologies such as SBG will be essential for genotyping of model and non-model genomes alike.