Block copolymer-oligonucleotide conjugates for genotyping on microarrays

Polymer-oligonucleotide conjugates were synthesized from the amphiphilic block copolymer poly(tert-butylacrylamide-b-(N-acryloylmorpholine-co-N-acryloxysuccinimide)) using an original solid-phase DNA synthesis strategy. This method provided conjugates highly functionalized with oligonucleotides throughout the polymer chain. After purification, block copolymer-oligonucleotide conjugates were spotted on a multidetection microarray system developed by Apibio using a standard nanodroplet piezo inkjet spotting technique to develop the oligosorbent assay (OLISA). Two genotyping models (HLA-DQB1 and platelet glycoproteins [GPs]), which are particularly difficult to study with standard systems, were evaluated. For both models, block copolymer-oligonucleotide conjugates used as capture probes amplified the responses of in vitro diagnostic assays. The detection limit reached by using conjugates was estimated at 15 pM for a 219-bp DNA target (HLA-DQB1 model). Moreover, single nucleotide polymorphism was detected in the platelet GPs genotyping model. The use of polymer conjugates led to a significant improvement in both sensitivity and specificity of standard hybridization assays even when applied to complex biological models.     

Allelic dosage analysis with genotyping microarrays

Genomic alternations, including dosage and allelic imbalance, constitute a major basis of neoplastic and other genetic disorders. Using oligonucleotide genotyping microarrays, here we report the development and usage of an algorithm, called genome imbalance map (GIM) algorithm, for allelic as well as total gene dosage analysis. Using the GIM algorithm, global genome imbalance status at over 100,000 loci was simultaneously analyzed with unprecedented accuracy and allelic discrimination.     

A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays

MOTIVATION: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. RESULTS: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. AVAILABILITY: R functions are available upon request from the authors.     

High throughput tissue preparation for large-scale genotyping experiments

A fast, efficient technique is described for the extraction of DNA from a large number of samples. The applications of this method include population genetics, plant breeding, and genetic screening. In the field, samples are collected in premeasured silica gel aliquots in polypropylene blocks, which are later used to grind the dried tissue. This permits naturalists, breeders, and collaborators to collect a large number of samples in a short amount of time and allows the samples to dry quickly during shipping. No phenol or chloroform steps are required to obtain high-quality DNA. Samples representing 12 plant families, three invertebrates, and a mammal were included. Quantities of DNA obtained were consistent with or better than other techniques. The quality of samples was tested by amplification of the internal transcribed spacer region. Test amplifications were successful, confirming the quality of extracted DNA.     

Construction of microarrays for genotyping of DQA using unmodified 45-mer oligonucleotide

The human leukocyte antigen (HLA) class II system is strongly connected to immunological response and its compatibility between tissues is critical in transplantation. The simple robust typing analyses of HLA genes are extremely important. In this paper, we developed an approach based on microarray technology for genotyping of DQA gene. The microarrays were constructed with a total 31 unmodified 45-mer oligonucleotide. The second exon of DQA gene was amplified, and allowed to hybridize with the array. DQA genotypes were assigned by quantitative analysis of the hybridization results. The arrays were evaluated by DQA genotyping of nine reference samples and 120 clinical samples. The results demonstrate that the genotyping accuracy/concordance achieved 97.5% compared with the direct DNA sequencing. Although our methods did not perform high-resolution genotyping, it could be an alternative for serological typing in routine medical practice.     

Noise reduction from genotyping microarrays using probe level information

Genomic copy number change is one of the important phenomenon observed in cancer and other genetic disorders. Recently oligonucleotide microarrays have been used to analyze changes in the copy number. Although high density microarrays provide genome wide useful data on copy number, they are often associated with substantial amount of experimental noise that could affect the performance of the analyses. We used the high density oligonucleotide genotyping microarrays in our experiments that uses redundant probe tiling approach for individual SNPs. We found that the noise in the genotyping microarray data is associated with several experimental steps during target preparation and devised an algorithm that takes into account those experimental parameters. Additionally, defective probes that do not hybridize well to the target and therefore could not be modified inherently were detected and omitted automatically by using the algorithm. When we applied the algorithm to actual datasets, we could reduce the noise substantially without compressing the dynamic range. Additionally, combinatorial use of our noise reduction algorithm and conventional breakpoint detection algorithm successfully detected a microamplification of c-myc which was overlooked in the raw data. The algorithm described here is freely available with the software upon request to all non-profit researchers.     

Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays

MOTIVATION: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample. RESULTS: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. AVAILABILITY: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website.     

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of approximately 160,000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300,000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from http://www2.umdnj.edu/lilabweb/publications/AccuTyping.html.     

High-resolution genotyping via whole genome hybridizations to microarrays containing long oligonucleotide probes

To date, microarray-based genotyping of large, complex plant genomes has been complicated by the need to perform genome complexity reduction to obtain sufficiently strong hybridization signals. Genome complexity reduction techniques are, however, tedious and can introduce unwanted variables into genotyping assays. Here, we report a microarray-based genotyping technology for complex genomes (such as the 2.3 GB maize genome) that does not require genome complexity reduction prior to hybridization. Approximately 200,000 long oligonucleotide probes were identified as being polymorphic between the inbred parents of a mapping population and used to genotype two recombinant inbred lines. While multiple hybridization replicates provided ～97% accuracy, even a single replicate provided ～95% accuracy. Genotyping accuracy was further increased to >99% by utilizing information from adjacent probes. This microarray-based method provides a simple, high-density genotyping approach for large, complex genomes.     

CNstream: A method for the identification and genotyping of copy number polymorphisms using Illumina microarrays

Background  

Understanding the genetic basis of disease risk in depth requires an exhaustive knowledge of the types of genetic variation. Very recently, Copy Number Variants (CNVs) have received much attention because of their potential implication in common disease susceptibility. Copy Number Polymorphisms (CNPs) are of interest as they segregate at an appreciable frequency in the general population (i.e. > 1%) and are potentially implicated in the genetic basis of common diseases.     

Use of a multi-thermal washer for DNA microarrays simplifies probe design and gives robust genotyping assays

DNA microarrays are generally operated at a single condition, which severely limits the freedom of designing probes for allele-specific hybridization assays. Here, we demonstrate a fluidic device for multi-stringency posthybridization washing of microarrays on microscope slides. This device is called a multi-thermal array washer (MTAW), and it has eight individually controlled heating zones, each of which corresponds to the location of a subarray on a slide. Allele-specific oligonucleotide probes for nine mutations in the beta-globin gene were spotted in eight identical subarrays at positions corresponding to the temperature zones of the MTAW. After hybridization with amplified patient material, the slides were mounted in the MTAW, and each subarray was exposed to different temperatures ranging from 22 to 40°C. When processed in the MTAW, probes selected without considering melting temperature resulted in improved genotyping compared with probes selected according to theoretical melting temperature and run under one condition. In conclusion, the MTAW is a versatile tool that can facilitate screening of a large number of probes for genotyping assays and can also enhance the performance of diagnostic arrays.     

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.     

Non-toxic and efficient DNA extractions for soybean leaf and seed chips for high-throughput and large-scale genotyping

In applied soybean (Glycine max L.) breeding programs, marker-assisted selection has become a necessity to select value-added quantitative trait loci. The goal of this work was to improve marker-assisted selection workflow by developing a reliable, inexpensive, high-throughput DNA extraction protocol for soybean seed and leaf samples that does not generate hazardous waste. The DNA extraction protocol developed allows for the leverage of robust SNP genotyping platforms such as the Simple Probe Assay and KASPar v4.0 SNP Genotyping System to genotype thousands of seeds or leaves non-destructively in a single day with a 95 % success rate. This methodology makes it possible to run up to 150 SNP markers on the DNA extracted from a single seed chip or leaf sample.     

Development of a single tube 640-plex genotyping method for detection of nucleic acid variations on microarrays

Detection of DNA sequence variation is critical to biomedical applications, including disease genetic identification, diagnosis and treatment, drug discovery and forensic analysis. Here, we describe an arrayed primer extension-based genotyping method (APEX-2) that allows multiplex (640-plex) DNA amplification and detection of single nucleotide polymorphisms (SNPs) and mutations on microarrays via four-color single-base primer extension. The founding principle of APEX-2 multiplex PCR requires two oligonucleotides per SNP/mutation to generate amplicons containing the position of interest. The same oligonucleotides are then subsequently used as immobilized single-base extension primers on a microarray. The method described here is ideal for SNP or mutation detection analysis, molecular diagnostics and forensic analysis. This robust genetic test has minimal requirements: two primers, two spots on the microarray and a low cost four-color detection system for the targeted site; and provides an advantageous alternative to high-density platforms and low-density detection systems.     

Simulation of normal, carrier and affected controls for large-scale genotyping of cattle for factor XI deficiency

An insertion mutation within exon 12 of the factor XI gene has been described in Holstein cattle. This has opened the prospect for large-scale screening of cattle using the polymerase chain reaction (PCR) technique for the rapid identification of heterozygous animals. To facilitate such a screening process, the mutant and normal alleles of factor XI gene, represented by 244- and 320-bp PCR amplified fragments, were individually cloned in Escherichia coli using a multicopy plasmid cloning vehicle to generate pFXI-N and pFXI-M, respectively. The authenticity of the inserts was confirmed by nucleotide sequencing. A nested PCR method was developed, by which PCR amplicons generated from primers with annealing sites on the recombinant plasmids and by flanking the insert were used as templates for amplification of the diagnostic products using factor XI gene-specific primers. An equimolar mixture of both PCR amplicons, originating from pFXI-N and pFXI-M, constituted the carrier control while the individual amplicons were the affected and normal controls. The controls were used as references for in-gel comparison to screen a population of 307 cattle and 259 water buffaloes; the frequency of the mutant allele was found to be 0. No DNA size standards were required in this study. The simulated control DNA samples representing normal, carrier and affected cattle have the potential to help in large-scale screening of a cattle population for individuals that are carriers or affected by factor XI deficiency.     

A method to address differential bias in genotyping in large-scale association studies

In a previous paper we have shown that, when DNA samples for cases and controls are prepared in different laboratories prior to high-throughput genotyping, scoring inaccuracies can lead to differential misclassification and, consequently, to increased false-positive rates. Different DNA sourcing is often unavoidable in large-scale disease association studies of multiple case and control sets. Here, we describe methodological improvements to minimise such biases. These fall into two categories: improvements to the basic clustering methods for identifying genotypes from fluorescence intensities, and use of "fuzzy" calls in association tests in order to make appropriate allowance for call uncertainty. We find that the main improvement is a modification of the calling algorithm that links the clustering of cases and controls while allowing for different DNA sourcing. We also find that, in the presence of different DNA sourcing, biases associated with missing data can increase the false-positive rate. Therefore, we propose the use of "fuzzy" calls to deal with uncertain genotypes that would otherwise be labeled as missing.     

Quantitative trait loci for IQ and other complex traits: single-nucleotide polymorphism genotyping using pooled DNA and microarrays

Similar to other complex traits, it is likely that many DNA polymorphisms of small effect size [quantitative trait loci (QTLs)] are responsible for the high heritability of intelligence, in addition to many rare monogenic disorders known to contribute to lowered intelligence. We review the current status of approaches to identify QTLs associations for intelligence employing genome-wide strategies using pooled DNA from many individuals and evaluate the innovative approach of microarray analysis to genotype DNA pools for large numbers of single nucleotide polymorphisms.     

Photoimmobilization for microarrays

A photoimmobilization method has been developed for the preparation of microarray biochips. This photoimmobilization method makes it possible to easily covalently immobilize various types of organic molecules and cells on a chip. In addition, by using hydrophilic polymers as matrixes, it is possible to reduce nonspecific interactions with biological components. Various proteins, antibodies, and cells have been microarrayed using this technique, and interactions between these proteins, antibodies, and cells have been investigated. This type of microarray biochip will be important for academic applications such as genomics, proteomics, and cellomics, and clinical analyses.     

TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays

Background  

High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses.     

Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays

We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.