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.     

Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping

Background

Natural selection has molded evolution across all taxa. At an arguable date of around 330,000 years ago there were already at least two different types of cattle that became ancestors of nearly all modern cattle, the Bos taurus taurus more adapted to temperate climates and the tropically adapted Bos taurus indicus. After domestication, human selection exponentially intensified these differences. To better understand the genetic differences between these subspecies and detect genomic regions potentially under divergent selection, animals from the International Bovine HapMap Experiment were genotyped for over 770,000 SNP across the genome and compared using smoothed F_ST. The taurine sample was represented by ten breeds and the contrasting zebu cohort by three breeds.

Results

Each cattle group evidenced similar numbers of polymorphic markers well distributed across the genome. Principal components analyses and unsupervised clustering confirmed the well-characterized main division of domestic cattle. The top 1% smoothed F_ST, potentially associated to positive selection, contained 48 genomic regions across 17 chromosomes. Nearly half of the top F_ST signals (n = 22) were previously detected using a lower density SNP assay. Amongst the strongest signals were the BTA7:~50 Mb and BTA14:~25 Mb; both regions harboring candidate genes and different patterns of linkage disequilibrium that potentially represent intrinsic differences between cattle types. The bottom 1% of the smoothed F_ST values, potentially associated to balancing selection, included 24 regions across 13 chromosomes. These regions often overlap with copy number variants, including the highly variable region at BTA23:~24 Mb that harbors a large number of MHC genes. Under these regions, 318 unique Ensembl genes are annotated with a significant overrepresentation of immune related pathways.

Conclusions

Genomic regions that are potentially linked to purifying or balancing selection processes in domestic cattle were identified. These regions are of particular interest to understand the natural and human selective pressures to which these subspecies were exposed to and how the genetic background of these populations evolved in response to environmental challenges and human manipulation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-876) contains supplementary material, which is available to authorized users.     

Development and validation of the Axiom®Apple480K SNP genotyping array

Cultivated apple (Malus × domestica Borkh.) is one of the most important fruit crops in temperate regions, and has great economic and cultural value. The apple genome is highly heterozygous and has undergone a recent duplication which, combined with a rapid linkage disequilibrium decay, makes it difficult to perform genome‐wide association (GWA) studies. Single nucleotide polymorphism arrays offer highly multiplexed assays at a relatively low cost per data point and can be a valid tool for the identification of the markers associated with traits of interest. Here, we describe the development and validation of a 487K SNP Affymetrix Axiom^® genotyping array for apple and discuss its potential applications. The array has been built from the high‐depth resequencing of 63 different cultivars covering most of the genetic diversity in cultivated apple. The SNPs were chosen by applying a focal points approach to enrich genic regions, but also to reach a uniform coverage of non‐genic regions. A total of 1324 apple accessions, including the 92 progenies of two mapping populations, have been genotyped with the Axiom^®Apple480K to assess the effectiveness of the array. A large majority of SNPs (359 994 or 74%) fell in the stringent class of poly high resolution polymorphisms. We also devised a filtering procedure to identify a subset of 275K very robust markers that can be safely used for germplasm surveys in apple. The Axiom^®Apple480K has now been commercially released both for public and proprietary use and will likely be a reference tool for GWA studies in apple.     

Development,validation and genetic analysis of a large soybean SNP genotyping array

Cultivated soybean (Glycine max) suffers from a narrow germplasm relative to other crop species, probably because of under‐use of wild soybean (Glycine soja) as a breeding resource. Use of a single nucleotide polymorphism (SNP) genotyping array is a promising method for dissecting cultivated and wild germplasms to identify important adaptive genes through high‐density genetic mapping and genome‐wide association studies. Here we describe a large soybean SNP array for use in diversity analyses, linkage mapping and genome‐wide association analyses. More than four million high‐quality SNPs identified from high‐depth genome re‐sequencing of 16 soybean accessions and low‐depth genome re‐sequencing of 31 soybean accessions were used to select 180 961 SNPs for creation of the Axiom^® SoyaSNP array. Validation analysis for a set of 222 diverse soybean lines showed that 170 223 markers were of good quality for genotyping. Phylogenetic and allele frequency analyses of the validation set data indicated that accessions showing an intermediate morphology between cultivated and wild soybeans collected in Korea were natural hybrids. More than 90 unanchored scaffolds in the current soybean reference sequence were assigned to chromosomes using this array. Finally, dense average spacing and preferential distribution of the SNPs in gene‐rich chromosomal regions suggest that this array may be suitable for genome‐wide association studies of soybean germplasm. Taken together, these results suggest that use of this array may be a powerful method for soybean genetic analyses relating to many aspects of soybean breeding.     

Genomic prediction of breeding values using previously estimated SNP variances

Background

Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely.

Methods

SNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors.

Results

BLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10⁻⁸ instead of 10⁻¹⁰ for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS.

Conclusions

Combining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0052-x) contains supplementary material, which is available to authorized users.     

Objective evaluation measures of genetic marker selection in large-scale SNP genotyping

High-throughput single nucleotide polymorphism (SNP) genotyping systems provide two kinds of fluorescent signals detected from different alleles. In current technologies, the process of genotype discrimination requires subjective judgments by expert operators, even when using clustering algorithms. Here, we propose two evaluation measures to manage fluorescent scatter data with nonclear plot aggregation. The first is the marker ranking measure, which provides a ranking system for the SNP markers based on the distance between the scatter plot distribution and a user-defined ideal distribution. The second measure, called individual genotype membership, uses the membership probability of each genotype related to an individual plot in the scatter data. In verification experiments, the marker ranking measure determined the ranking of SNP markers correlated with the subjective order of SNP markers judged by an expert operator. The experiment using the individual genotype membership measure clarified that the total number of unclassified individuals was remarkably reduced compared to that of manually unclassified ones. These two evaluation measures were implemented as the GTAssist software. GTAssist provides objective standards and avoids subjective biases in SNP genotyping workflows.     

SNPchip: R classes and methods for SNP array data

High-density single nucleotide polymorphism microarrays (SNP chips) provide information on a subject's genome, such as copy number and genotype (heterozygosity/homozygosity) at a SNP. While fluorescence in situ hybridization and karyotyping reveal many abnormalities, SNP chips provide a higher resolution map of the human genome that can be used to detect, e.g., aneuploidies, microdeletions, microduplications and loss of heterozygosity (LOH). As a variety of diseases are linked to such chromosomal abnormalities, SNP chips promise new insights for these diseases by aiding in the discovery of such regions, and may suggest targets for intervention. The R package SNPchip contains classes and methods useful for storing, visualizing and analyzing high density SNP data. Originally developed from the SNPscan web-tool, SNPchip utilizes S4 classes and extends other open source R tools available at Bioconductor. This has numerous advantages, including the ability to build statistical models for SNP-level data that operate on instances of the class, and to communicate with other R packages that add additional functionality. AVAILABILITY: The package is available from the Bioconductor web page at www.bioconductor.org. SUPPLEMENTARY INFORMATION: The supplementary material as described in this article (case studies, installation guidelines and R code) is available from http://biostat.jhsph.edu/~iruczins/publications/sm/     

Genomic breeding value prediction: methods and procedures

Animal breeding faces one of the most significant changes of the past decades - the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.     

A genome-wide SNP genotyping array reveals patterns of global and repeated species-pair divergence in sticklebacks

Genes underlying repeated adaptive evolution in natural populations are still largely unknown. Stickleback fish (Gasterosteus aculeatus) have undergone a recent dramatic evolutionary radiation, generating numerous examples of marine-freshwater species pairs and a small number of benthic-limnetic species pairs found within single lakes [1]. We have developed a new genome-wide SNP genotyping array to study patterns of genetic variation in sticklebacks over a wide geographic range, and to scan the genome for regions that contribute to repeated evolution of marine-freshwater or benthic-limnetic species pairs. Surveying 34 global populations with 1,159 informative markers revealed substantial genetic variation, with predominant patterns reflecting demographic history and geographic structure. After correcting for geographic structure and filtering for neutral markers, we detected large repeated shifts in allele frequency at some loci, identifying both known and novel loci likely contributing to marine-freshwater and benthic-limnetic divergence. Several novel loci fall close to genes implicated in epithelial barrier or immune functions, which have likely changed as sticklebacks adapt to contrasting environments. Specific alleles differentiating sympatric benthic-limnetic species pairs are shared in nearby solitary populations, suggesting an allopatric origin for adaptive variants and selection pressures unrelated to sympatry in the initial formation of these classic vertebrate species pairs.     

Genomic profiling by DNA amplification of laser capture microdissected tissues and array CGH

Comparative genomic hybridization by means of BAC microarrays (array CGH) allows high-resolution profiling of copy-number aberrations in tumor DNA. However, specific genetic lesions associated with small but clinically relevant tumor areas may pass undetected due to intra-tumor heterogeneity and/or the presence of contaminating normal cells. Here, we show that the combination of laser capture microdissection, 29 DNA polymerase-mediated isothermal genomic DNA amplification, and array CGH allows genomic profiling of very limited numbers of cells. Moreover, by means of simple statistical models, we were able to bypass the exclusion of amplification distortions and variability prone areas, and to detect tumor-specific chromosomal gains and losses. We applied this new combined experimental and analytical approach to the genomic profiling of colorectal adenomatous polyps and demonstrated our ability to accurately detect single copy gains and losses affecting either whole chromosomes or small genomic regions from as little as 2 ng of DNA or 1000 microdissected cells.     

SNP array analysis in constitutional and cancer genome diagnostics--copy number variants, genotyping and quality control

Array-based comparative genomic hybridization analysis of genomic DNA was first applied in postnatal diagnosis for patients with intellectual disability (ID) and/or congenital anomalies (CA). Genome-wide single-nucleotide polymorphism (SNP) array analysis was subsequently implemented as the first line diagnostic test for ID/CA patients in our laboratory in 2009, because its diagnostic yield is significantly higher than that of routine cytogenetic analysis. In addition to the detection of copy number variations, the genotype information obtained with SNP array analysis enables the detection of stretches of homozygosity and thereby the possible identification of recessive disease genes, mosaic aneuploidy, or uniparental disomy. Patient-parent (trio) information analysis is used to screen for the presence of any form of uniparental disomy in the patient and can determine the parental origin of a de novo copy number variation. Moreover, the outcome of a genotype analysis is used as a final quality control by ruling out potential sample mismatches due to non-paternity or sample mix-up. SNP array analysis is now also used in our laboratory for patients with disorders for which locus heterogeneity is known (homozygosity pre-screening), in prenatal diagnosis in case of structural ultrasound anomalies, and for patients with leukemia. In this report, we summarize our array findings and experiences in the various diagnostic applications and demonstrate the power of a SNP-based array platform for molecular karyotyping, because it not only significantly improves the diagnostic yield in both constitutional and cancer genome diagnostics, but it also enhances the quality of the diagnostic laboratory workflow.     

SEAN: SNP prediction and display program utilizing EST sequence clusters

SEAN is an application that predicts single nucleotide polymorphisms (SNPs) using multiple sequence alignments produced from expressed sequence tag (EST) clusters. The algorithm uses rules of sequence identity and SNP abundance to determine the quality of the prediction. A Java viewer is provided to display the EST alignments and predicted SNPs.     

The application of genome-wide SNP genotyping methods in studies on livestock genomes

Animal genomics is currently undergoing dynamic development, which is driven by the flourishing of high-throughput genome analysis methods. Recently, a large number of animals has been genotyped with the use of whole-genome genotyping assays in the course of genomic selection programmes. The results of such genotyping can also be used for studies on different aspects of livestock genome functioning and diversity. In this article, we review the recent literature concentrating on various aspects of animal genomics, including studies on linkage disequilibrium, runs of homozygosity, selection signatures, copy number variation and genetic differentiation of animal populations. Our work is aimed at providing insight into certain achievements of animal genomics and to arouse interest in basic research on the complexity and structure of the genomes of livestock.     

Recent patents on high-throughput single nucleotide polymorphism (SNP) genotyping methods

Single nucleotide polymorphisms (SNPs) are single-base inheritable variations in a given and defined genetic location that occur in at least 1% of the population. SNPs are useful markers for genetic association studies in disease susceptibility or adverse drug reactions, in evolutionary studies and forensic science. Given the potential impact of SNPs, the biotechnology industry has focused on the development of high-throughput methods for SNP genotyping. Many highthroughput SNP genotyping technologies are currently available and many others are being patented recently. Each offers a unique combination of scale, accuracy, throughput and cost. In this review, we described some of the most important recent SNP genotyping methods and also recent patents associated with it.