Single Nucleotide Polymorphism Genotype Calls Research Articles

Non-invasive DNA collection for parentage analysis for bivalves: A case study from the silver-lipped pearl oyster (Pinctada maxima)

Due to the high revenues generated by the pearl industry, interest in developing breeding programs is increasing. The traditional approach for collecting DNA for pedigree analyses in breeding programs for the silver-lipped pearl oyster (Pinctada maxima) involves tissue excision from the muscular foot and/or mantle. Tissue excision causes injury and stress to the animal, and can lead to reduced growth efficiency, bacterial infection and subsequent mortality when oysters are returned to the non-sterile marine environment. The approach of swabbing tissue to collect cells for DNA analyses offers a potential non-invasive and rapid method but has not been reported previously for pearl oysters; particularly where DNA is destined for a high-throughput genotype-by-sequencing (GBS) single nucleotide polymorphism (SNP) genotyping platform. To evaluate the potential of swabbing as a method to collect DNA for downstream GBS SNP genotyping under commercial farming conditions, 29 broodstock pearl oysters were swabbed with two different swab brushes (a stiff and soft brush). Concentration of the extracted DNA from swabs was found to meet requirements for genotyping (44 to 59 ng/μL) and 260/280 ratios were in the acceptable range for DNA purity (1.9–2.2). SNP genotype calls derived from swab samples were highly correlated with those derived from tissue excised from the same oyster (mean of 0.97 ± 0.04 for soft bristle – mean of 0.98 ± 0.02 for stiff bristle). Finally, we evaluated the performance of swabbing under a commercial breeding program scenario, whereby 923 spat were genotyped and 18 months later 1927 pearl oysters from the same commercial cohort were swabbed to determine parental contributions for the cohort at the two time periods. Results demonstrated that the swab method is reliable for collecting sufficient quantity and quality of DNA for genome-wide SNP genotyping enabling accurate pedigree reconstruction. This study validated the efficiency of the use of the swab technique to collect DNA for routine parentage assignment, while limiting the adverse effects of destructive tissue sampling for commercial use and/or for the development of breeding programs.

Demonstration of the Use of Environmental DNA for the Non-Invasive Genotyping of a Bivalve Mollusk, the European Flat Oyster (Ostrea edulis).

Accurate SNP (single nucleotide polymorphism) genotype information is critical for a wide range of selective breeding applications in aquaculture, including parentage assignment, marker-assisted, and genomic selection. However, the sampling of tissue for genetic analysis can be invasive for juvenile animals or taxa where sampling tissue is difficult or may cause mortality (e.g. bivalve mollusks). Here, we demonstrate a novel, non-invasive technique for sampling DNA based on the collection of environmental DNA using European Flat Oysters (Ostrea edulis) as an example. The live animals are placed in individual containers until sufficient genetic material is released into the seawater which is then recovered by filtration. We compared the results of tissue and eDNA derived SNP genotype calls using a PCR based genotyping platform. We found that 100% accurate genotype calls from eDNA are possible, but depend on appropriate filtration and the dilution of the sample throughout the workflow. We also developed an additional low-cost DNA extraction technique which provided >99% correct SNP genotype calls in comparison to tissue. It was concluded that eDNA sampling can be used in hatchery and selective breeding programs applicable to any aquatic organism for which direct tissue sampling may result in animal welfare concerns or mortality.

Development and Applications of a High Throughput Genotyping Tool for Polyploid Crops: Single Nucleotide Polymorphism (SNP) Array.

Polypoid species play significant roles in agriculture and food production. Many crop species are polyploid, such as potato, wheat, strawberry, and sugarcane. Genotyping has been a daunting task for genetic studies of polyploid crops, which lags far behind the diploid crop species. Single nucleotide polymorphism (SNP) array is considered to be one of, high-throughput, relatively cost-efficient and automated genotyping approaches. However, there are significant challenges for SNP identification in complex, polyploid genomes, which has seriously slowed SNP discovery and array development in polyploid species. Ploidy is a significant factor impacting SNP qualities and validation rates of SNP markers in SNP arrays, which has been proven to be a very important tool for genetic studies and molecular breeding. In this review, we (1) discussed the pros and cons of SNP array in general for high throughput genotyping, (2) presented the challenges of and solutions to SNP calling in polyploid species, (3) summarized the SNP selection criteria and considerations of SNP array design for polyploid species, (4) illustrated SNP array applications in several different polyploid crop species, then (5) discussed challenges, available software, and their accuracy comparisons for genotype calling based on SNP array data in polyploids, and finally (6) provided a series of SNP array design and genotype calling recommendations. This review presents a complete overview of SNP array development and applications in polypoid crops, which will benefit the research in molecular breeding and genetics of crops with complex genomes.

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.

A Single-Array-Based Method for Detecting Copy Number Variants Using Affymetrix High Density SNP Arrays and its Application to Breast Cancer.

Cumulative evidence has shown that structural variations, due to insertions, deletions, and inversions of DNA, may contribute considerably to the development of complex human diseases, such as breast cancer. High-throughput genotyping technologies, such as Affymetrix high density single-nucleotide polymorphism (SNP) arrays, have produced large amounts of genetic data for genome-wide SNP genotype calling and copy number estimation. Meanwhile, there is a great need for accurate and efficient statistical methods to detect copy number variants. In this article, we introduce a hidden-Markov-model (HMM)-based method, referred to as the PICR-CNV, for copy number inference. The proposed method first estimates copy number abundance for each single SNP on a single array based on the raw fluorescence values, and then standardizes the estimated copy number abundance to achieve equal footing among multiple arrays. This method requires no between-array normalization, and thus, maintains data integrity and independence of samples among individual subjects. In addition to our efforts to apply new statistical technology to raw fluorescence values, the HMM has been applied to the standardized copy number abundance in order to reduce experimental noise. Through simulations, we show our refined method is able to infer copy number variants accurately. Application of the proposed method to a breast cancer dataset helps to identify genomic regions significantly associated with the disease.

Short communication: Relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle

Call rates on both a single nucleotide polymorphism (SNP) basis and an animal basis are used as measures of data quality and as screening tools for genomic studies and evaluations of dairy cattle. To investigate the relationship of SNP call rate and genotype accuracy for individual SNP, the correlation between percentages of missing genotypes and parent-progeny conflicts for each SNP was calculated for 103,313 Holsteins. Correlations ranged from 0.14 to 0.38 for the BovineSNP50 and BovineLD (Illumina Inc., San Diego, CA) and GeneSeek Genomic Profiler (Neogen Corp., Lincoln, NE) chips, with lower correlations for newer chips. For US genomic evaluations, genotypes are excluded for animals with a call rate of <90% across autosomal SNP or <80% across X-specific SNP. Mean call rate for 220,175 Holstein, Jersey, and Brown Swiss genotypes was 99.6%. Animal genotypes with a call rate of ≤99% were examined from the US Department of Agriculture genotype database to determine how genotype call rate is related to accuracy of calls on an animal basis. Animal call rate was determined from SNP used in genomic evaluation and is the number of called autosomal and X-specific SNP genotypes divided by the number of SNP from that type of chip. To investigate the relationship of animal call rate and parentage validation, conflicts between a genotyped animal and its sire or dam were determined through a duo test (opposite homozygous SNP genotypes between sire and progeny; 1,374 animal genotypes) and a trio test (also including conflicts with dam and heterozygous SNP genotype for the animal when both parents are the same homozygote; 482 animal genotypes). When animal call rate was ≤80%, parentage validation was no longer reliable with the duo test. With the trio test, parentage validation was no longer reliable when animal call rate was ≤90%. To investigate how animal call rate was related to genotyping accuracy for animals with multiple genotypes, concordance between genotypes for 1,216 animals that had a genotype with a call rate of ≤99% (low call rate) as well as a genotype with a call rate of >99% (high call rate) were calculated by dividing the number of identical SNP genotype calls by the number of SNP that were called for both genotypes. Mean concordance between low- and high-call genotypes was >99% for a low call rate of >90% but decreased to 97% for a call rate of 86 to 90% and to 58% for a call rate of <60%. Edits on call rate reduce the use of incorrect SNP genotypes to calculate genomic evaluations.

Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data

Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.

A study of biases of DNA copy number estimation based on PICR model

Affymetrix single-nucleotide polymorphism (SNP) arrays have been widely used for SNP genotype calling and copy number variation (CNV) studies, both of which are dependent on accurate DNA copy number estimation significantly. However, the methods for copy number estimation may suffer from kinds of difficulties: probe dependent binding affinity, crosshybridization of probes, and the whole genome amplification (WGA) of DNA sequences. The probe intensity composite representation (PICR) model, one former established approach, can cope with most complexities and achieve high accuracy in SNP genotyping. Nevertheless, the copy numbers estimated by PICR model still show array and site dependent biases for CNV studies. In this paper, we propose a procedure to adjust the biases and then make CNV inference based on both PICR model and our method. The comparison indicates that our correction of copy numbers is necessary for CNV studies.

Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation

Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms.

A simple method for comparing microarray genotype data between brain and other tissues

A number of software packages are currently available to analyse the large amounts of single nucleotide polymorphism (SNP) data generated using microarrays. However, for less usual datasets, such as those involving DNA from different types of tissue in cases and controls, investigators need a program that can be tailored to their particular needs. The purpose of this study was to demonstrate a flexible method of analysing SNP data derived from Affymetrix 500K GeneChips without the need for an array-dedicated software package. SNP genotype calls from white cell and brain DNA samples from patients with amyotrophic lateral sclerosis and controls were imported into a Microsoft Access ® database and analysed in two ways. The first used database “queries” designed through the Access graphical user interface. The second involved scripts written with the Visual Basic software that is supplied with Access. Both the queries and scripts performed simple two-way comparisons as well as three-way comparisons between genotypes, which is vital when comparing genotypes in different tissues in cases and controls. A similar method could be used to compare copy number changes derived from SNP intensities between multiple datasets. This flexible method can easily be customised by the investigator and would be of use in comparing microarray SNP data between multiple datasets, in particular where somatic mosaicism was suspected.

Analysis of Loss of Heterozygosity in Adult Patients with De Novo Acute Myeloid Leukemia and Normal Karyotype.

A large proportion of acute myeloid leukemia (AML) exhibits a normal karyotype in which the underlying pathomechanisms still have to be determined. Novel techniques like arrayCGH or single nucleotide polymorphism (SNP) chip analysis allow the identification and characterization of molecular rearrangements at the sub-megabase level. Recently, the application of genome-wide SNP array technology revealed frequent uniparental disomy (UPD) in approximately 20% of AML suggesting that UPD represents a nonrandom event in leukemogenesis. Uniparental disomy is acquired by somatic recombination and therefore not accessible by conventional cytogenetic methods or arrayCGH. In this study we analyzed DNA from AML patients with normal karyotype for the presence of LOH. SNP analysis was performed on the Mapping 100k GeneChip (Affymetrix, Santa Clara, CA). DNA was extracted from paired samples of 56 de novo AML patients with normal karyotype at diagnosis and in complete remission, respectively. Signal intensity data were analyzed by the GCOS GeneChip analysis software and statistical analysis of SNP call data was performed by the dChipSNP software. In addition, standard mutation screening of the genes encoding NPM1, FLT3, CEBPA, MLL and NRAS was performed in all cases. Using the 100k SNP array, a mean SNP call rate of 98.2% was reached, resulting in > 110,000 SNP genotype calls per sample. Signal intensity data analysis revealed submicroscopic chromosomal deletions resulting in hemizygosity in three patients. Patient 1 had a single 2 Mb deletion in chromosomal band 3p14.1, patient 2 had two small deletions affecting chromosome 12q23 and 12p13, the latter encompassing the ETV6 locus, and patient 3 had two small deletions within the long arm of chromosome 8. Besides these small chromosomal regions of copy number alterations, we found 4 large stretches of somatically acquired homozygosity without numeric alterations, affecting chromosome 6 (6p21 to 6 pter and 6q26 to 6 qter), chromosome 11 (11p12 to 11pter) and chromosome 13 (13q11 to 13qter). Noteworthy, in the case with uniparental disomy of chromosome 13, we could detect a homozygous FLT3-ITD mutation, supporting the findings that acquired isodisomy for chromosome 13 is common in AML, and associated with FLT3-ITD mutations (Griffiths et al., Leukemia, 2005). In summary, high resolution SNP assay technology in AML patients with normal karyotype allowed the identification of distinct chromosomal regions affected by UPD, supporting the postulated nonrandom mechanism of acquired mitotic recombination events in AML. Besides known chromosomal regions known to be affected by genomic aberrations in AML, we found additional submicroscopic chromosomal aberrations in cases with normal karyotype. Analysis of larger patient series will allow the identification of novel regions of interest harboring genes that might be involved in the pathogenesis of AML.

Tumor classification based on DNA copy number aberrations determined using SNP arrays

High-density single nucleotide polymorphism (SNP) array is a recently introduced technology that genotypes more than 10,000 human SNPs on a single array. It has been shown that SNP arrays can be used to determine not only SNP genotype calls, but also DNA copy number (DCN) aberrations, which are common in solid tumors. In the past, effective cancer classification has been demonstrated using microarray gene expression data, or DCN data derived from comparative genomic hybridization (CGH) arrays. However, the feasibility of cancer classification based on DCN aberrations determined by SNP arrays has not been previously investigated. In this study, we address this issue by applying state-of-the-art classification algorithms and feature selection algorithms to the DCN aberration data derived from a public SNP array dataset. Performance was measured via leave-one-out cross-validation (LOOCV) classification accuracy. Experimental results showed that the maximum accuracy was 73.33%, which is comparable to the maximum accuracy of 76.5% based on CGH-derived DCN data reported previously in the literature. These results suggest that DCN aberration data derived from SNP arrays is useful for etiology-based tumor classification.

MACGT: multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing data

Multi-dimensional Automated Clustering Genotyping Tool (MACGT) is a Java application that clusters complex multi-dimensional vector data derived from single nucleotide polymorphism (SNP) genotyping experiments using mini-sequencing based microarray chemistries such as arrayed primer extension (APEX). Spot intensity output files from microarray experiments across multiple samples are imported into MACGT. The datasets can include four channels of intensity data for each spot, replica spots for each SNP probe and multiple probe types (APEX and allele-specific APEX probes) on both DNA strands for each SNP. MACGT automatically clusters these multi-dimensionality datasets for each SNP across multiple samples. Incorporation of additional array datasets from known samples that have previously validated SNP genotype calls allows unknown samples to be automatically assigned a genotype based on the clustering, along with numerical measures of confidence for each genotype call. Calling accuracy by MACGT exceeds 98% when applied to genotyping data from APEX microarrays, and can be increased to >99.5% by applying thresholds to the confidence measures.

Extent and Distribution of Linkage Disequilibrium in Three Genomic Regions

The positional cloning of genes underlying common complex diseases relies on the identification of linkage disequilibrium (LD) between genetic markers and disease. We have examined 127 polymorphisms in three genomic regions in a sample of 575 chromosomes from unrelated individuals of British ancestry. To establish phase, 800 individuals were genotyped in 160 families. The fine structure of LD was found to be highly irregular. Forty-five percent of the variation in disequilibrium measures could be explained by physical distance. Additional factors, such as allele frequency, type of polymorphism, and genomic location, explained <5% of the variation. Nevertheless, disequilibrium was occasionally detectable at 500 kb and was present for over one-half of marker pairs separated by <50 kb. Although these findings are encouraging for the prospects of a genomewide LD map, they suggest caution in interpreting localization due to allelic association.