Precision-mapping and statistical validation of quantitative trait loci by machine learning

Background

DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.

Results

In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.

Conclusion

Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.     

SNP genotyping on a genome-wide amplified DOP-PCR template

With the increasing demand for higher throughput single nucleotide polymorphism (SNP) genotyping, the quantity of genomic DNA often falls short of the number of assays required. We investigated the use of degenerate oligonucleotide primed polymerase chain reaction (DOP-PCR) to generate a template for our SNP genotyping methodology of fluorescence polarization template-directed dye-terminator incorporation detection. DOP-PCR employs a degenerate primer (5′-CCGACTCGAGNNNNNNATGTGG-3′) to produce non-specific uniform amplification of DNA. This approach has been successfully applied to microsatellite genotyping. We compared genotyping of DOP-PCR-amplified genomic DNA to genomic DNA as a template. Results were analyzed with respect to feasibility, allele loss of alleles, genotyping accuracy and storage conditions in a high-throughput genotyping environment. DOP-PCR yielded overall satisfactory results, with a certain loss in accuracy and quality of the genotype assignments. Accuracy and quality of genotypes generated from the DOP-PCR template also depended on storage conditions. Adding carrier DNA to a final concentration of 10 ng/µl improved results. In conclusion, we have successfully used DOP-PCR to amplify our genomic DNA collection for subsequent SNP genotyping as a standard process.     

cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate

Cost-effective oligonucleotide genotyping arrays like the Affymetrix SNP 6.0 are still the predominant technique to measure DNA copy number variations (CNVs). However, CNV detection methods for microarrays overestimate both the number and the size of CNV regions and, consequently, suffer from a high false discovery rate (FDR). A high FDR means that many CNVs are wrongly detected and therefore not associated with a disease in a clinical study, though correction for multiple testing takes them into account and thereby decreases the study's discovery power. For controlling the FDR, we propose a probabilistic latent variable model, 'cn.FARMS', which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. On HapMap data, cn.FARMS clearly outperformed the two most prevalent methods with respect to sensitivity and FDR. The software cn.FARMS is publicly available as a R package at http://www.bioinf.jku.at/software/cnfarms/cnfarms.html.     

A quantitative assay to detect α-thalassemia deletions and triplications using multiplex nested real-time quantitative polymerase chain reaction

Increasing evidence indicates that copy number variants (CNVs) have great relevance to common human diseases. In α-thalassemia, clinical phenotypes are related to genotypes, specifically copy number changes in the human α-globin gene cluster. Assays are available for high-throughput screening of unknown CNVs genome-wide and also for targeted CNV genotyping at loci associated with genetic disorders. Here we describe a universal quantitative approach based on nested real-time quantitative polymerase chain reaction for accurate determination of copy numbers at multiple particular gene loci. We used the α-globin gene as a model system, obtaining the reproducibility and sensitivity to analyze different gene copies and testing 95 DNA samples with 16 different known genotypes. Our results showed that this approach has high sensitivity and low standard deviations for correctly genotyping DNA samples containing different copy numbers of the α1 and α2 globin genes. Our method is rapid, simple, and reliable, and it could be used to simultaneously screen for α-thalassemia deletions or triplications. Moreover, it has potential as a versatile technology for the rapid genotyping of known CNVs in a targeted region.     

Large-scale Genotyping and Genetic Mapping in Plasmodium Parasites

The completion of many malaria parasite genomes provides great opportunities for genomewide characterization of gene expression and high-throughput genotyping. Substantial progress in malaria genomics and genotyping has been made recently, particularly the development of various microarray platforms for large-scale characterization of the Plasmodium falciparum genome. Microarray has been used for gene expression analysis, detection of single nucleotide polymorphism (SNP) and copy number variation (CNV), characterization of chromatin modifications, and other applications. Here we discuss some recent advances in genetic mapping and genomic studies of malaria parasites, focusing on the use of high-throughput arrays for the detection of SNP and CNV in the P. falciparum genome. Strategies for genetic mapping of malaria traits are also discussed.     

A novel procedure for genotyping of single nucleotide polymorphisms in trisomy with genomic DNA and the invader assay

Individuals with trisomy 21 display complex phenotypes with differing degrees of severity. Numerous reliable methods have been established to diagnose the initial trisomy in these patients, but the identification and characterization of the genetic basis of the phenotypic variation in individuals with trisomy remains challenging. To date, methods that can accurately determine genotypes in trisomic DNA samples are expensive, require specialized equipment and complicated analyses. Here we report proof-of-concept results for an Invader® assay-based genotyping procedure that can determine SNP genotypes in trisomic genomic DNA samples in a simple and cost-effective manner. The procedure requires only two experimental steps: a real-time measurement of the fluorescent Invader® signal and analysis with a specifically designed clustering algorithm. The approach was tested using genomic DNA samples from 23 individuals with trisomy 21, and results were compared to genotypes previously determined with pyrosequencing. Additional assays for 15 SNPs were tested in a set of 21 DNA samples to assess assay performance. Our method successfully identified the correct SNP genotypes for the trisomic genomic DNA samples tested, and thus provides an alternative to determine SNP genotypes in trisomic DNA samples for subsequent association studies in patients with Down syndrome and other trisomies.     

An optimized microarray platform for assaying genomic variation in <Emphasis Type="Italic">Plasmodium falciparum</Emphasis> field populations

We present an optimized probe design for copy number variation (CNV) and SNP genotyping in the Plasmodium falciparum genome. We demonstrate that variable length and isothermal probes are superior to static length probes. We show that sample preparation and hybridization conditions mitigate the effects of host DNA contamination in field samples. The microarray and workflow presented can be used to identify CNVs and SNPs with 95% accuracy in a single hybridization, in field samples containing up to 92% human DNA contamination.     

Multiple displacement amplification prior to single nucleotide polymorphism genotyping in epidemiologic studies

We assessed the whole genome amplification strategy, known as multiple displacement amplification (MDA), for use with the TaqMan genotyping platform for DNA samples derived from two case-control studies nested in the Nurses' Health Study and the Physicians' Health Study. Our objectives were to (1) quantify DNA yield from samples of varying starting concentrations and (2) assess whether MDA products give an accurate representation of the original genomic sequence. Multiple displacement amplification yielded a mean 23000-fold increase in DNA quantity and genotyping results demonstrate 99.95% accuracy across six SNPs from four genes for 352 samples included in this study. These results suggest that MDA will provide a sufficiently robust amplification of limiting samples of genomic DNA that can be used for SNP genotyping in large case-control studies of complex diseases.     

Quantitative PCR assessment of microsatellite and SNP genotyping with variable quality DNA extracts

In this study we developed eight quantitative PCR (qPCR) assays to evaluate the starting copy number of nuclear and mitochondrial DNA fragments ranging from 75 to 350 base-pairs in DNA extracts from Chinook salmon tissues with varying quality. Samples were genotyped with 13 microsatellite and 29 SNP assays and average genotyping success for good, intermediate, and poor quality samples was 96%, 24%, and 24% for microsatellite loci, and 98%, 97%, and 79% for SNPs, respectively. As measured by qPCR, good quality samples had a consistently high number of starting copies across all fragment sizes with little change between the smallest and largest size. In contrast, the intermediate and poor quality samples displayed decreases in starting copy number as fragment size increased, and was most pronounced with poor samples. Logistic regression of genotyping success by starting copy number indicated that in order to achieve at least 90% genotyping success, approximately 1,000 starting copies of nuclear DNA are necessary for microsatellite loci, and as few as 14 starting copies for SNP assays (but we recommend at least 50 copies to reduce genotyping error). While these guidelines apply specifically to Chinook salmon and the genetic markers included in this study, the principles are transferable to other species and markers due to the underlying process associated with template quantity and PCR amplification.     

Allele-specific amplification in cancer revealed by SNP array analysis

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ.     

Identification of SNP markers for common CNV regions and association analysis of risk of subarachnoid aneurysmal hemorrhage in Japanese population

Copy number variation (CNV) is emerging as a new tool for understanding human genomic variation, but its relationship with human disease is not yet fully understood. The data for a total of 317,503 genotypes were collected for a genome-wide association study of subarachnoid aneurismal hemorrhage (SAH) in a Japanese population (cases and controls, n = 497) using Illumina HumanHap300 BeadChip^®. To identify multi-allelic CNV markers, we visually inspected all genotype clusters of 317,503 SNP markers covering the whole genome using Illumina’s BeadStudio 3.0^® software. As a result, we identified 597 multi-allelic CNV markers for common (copy loss frequency > 0.05) CNV regions in a Japanese population (n = 497). The identified CNV markers shared the following characteristics: enrichment of Hardy–Weinberg disequilibria, Mendelian inconsistency among families, and high missing genotype rate. All annotated information for those markers is summarized in our database (http://www.snp-genetics.com/user/srch.htm). In addition, we performed case-control association analyses of identified multi-allelic CNV markers with the risk of subarachnoid aneurysmal hemorrhage. One SNP marker (rs1242541) within a CNV region neighboring the Sel-1 suppressor of lin-12-like protein (SEL1L) was significantly associated with a risk of SAH (P = 0.0006). We also validated the CNV around rs1242541 using real-time quantitative polymerase chain reaction (PCR). Information and methods used in this study would be helpful for accurate genotyping of SNPs on CNV regions, which could be used for association analysis of SNP markers within CNV regions.     

MARA: a novel approach for highly multiplexed locus-specific SNP genotyping using high-density DNA oligonucleotide arrays

We have developed a locus-specific DNA target preparation method for highly multiplexed single nucleotide polymorphism (SNP) genotyping called MARA (Multiplexed Anchored Runoff Amplification). The approach uses a single primer per SNP in conjunction with restriction enzyme digested, adapter-ligated human genomic DNA. Each primer is composed of common sequence at the 5′ end followed by locus-specific sequence at the 3′ end. Following a primary reaction in which locus-specific products are generated, a secondary universal amplification is carried out using a generic primer pair corresponding to the oligonucleotide and genomic DNA adapter sequences. Allele discrimination is achieved by hybridization to high-density DNA oligonucleotide arrays. Initial multiplex reactions containing either 250 primers or 750 primers across nine DNA samples demonstrated an average sample call rate of ~95% for 250- and 750-plex MARA. We have also evaluated >1000- and 4000-primer plex MARA to genotype SNPs from human chromosome 21. We have identified a subset of SNPs corresponding to a primer conversion rate of ~75%, which show an average call rate over 95% and concordance >99% across seven DNA samples. Thus, MARA may potentially improve the throughput of SNP genotyping when coupled with allele discrimination on high-density arrays by allowing levels of multiplexing during target generation that far exceed the capacity of traditional multiplex PCR.     

The Role of Copy Number Variation in Susceptibility to Amyotrophic Lateral Sclerosis: Genome-Wide Association Study and Comparison with Published Loci

Background

The genetic contribution to sporadic amyotrophic lateral sclerosis (ALS) has not been fully elucidated. There are increasing efforts to characterise the role of copy number variants (CNVs) in human diseases; two previous studies concluded that CNVs may influence risk of sporadic ALS, with multiple rare CNVs more important than common CNVs. A little-explored issue surrounding genome-wide CNV association studies is that of post-calling filtering and merging of raw CNV calls. We undertook simulations to define filter thresholds and considered optimal ways of merging overlapping CNV calls for association testing, taking into consideration possibly overlapping or nested, but distinct, CNVs and boundary estimation uncertainty.

Methodology and Principal Findings

In this study we screened Illumina 300K SNP genotyping data from 730 ALS cases and 789 controls for copy number variation. Following quality control filters using thresholds defined by simulation, a total of 11321 CNV calls were made across 575 cases and 621 controls. Using region-based and gene-based association analyses, we identified several loci showing nominally significant association. However, the choice of criteria for combining calls for association testing has an impact on the ranking of the results by their significance. Several loci which were previously reported as being associated with ALS were identified here. However, of another 15 genes previously reported as exhibiting ALS-specific copy number variation, only four exhibited copy number variation in this study. Potentially interesting novel loci, including EEF1D, a translation elongation factor involved in the delivery of aminoacyl tRNAs to the ribosome (a process which has previously been implicated in genetic studies of spinal muscular atrophy) were identified but must be treated with caution due to concerns surrounding genomic location and platform suitability.

Conclusions and Significance

Interpretation of CNV association findings must take into account the effects of filtering and combining CNV calls when based on early genome-wide genotyping platforms and modest study sizes.     

Assessing the utility of whole genome amplified DNA for next‐generation molecular ecology

DNA quantity can be a hindrance in ecological and evolutionary research programmes due to a range of factors including endangered status of target organisms, available tissue type, and the impact of field conditions on preservation methods. A potential solution to low‐quantity DNA lies in whole genome amplification (WGA) techniques that can substantially increase DNA yield. To date, few studies have rigorously examined sequence bias that might result from WGA and next‐generation sequencing of nonmodel taxa. To address this knowledge deficit, we use multiple displacement amplification (MDA) and double‐digest RAD sequencing on the grey mouse lemur (Microcebus murinus) to quantify bias in genome coverage and SNP calls when compared to raw genomic DNA (gDNA). We focus our efforts in providing baseline estimates of potential bias by following manufacturer's recommendations for starting DNA quantities (>100 ng). Our results are strongly suggestive that MDA enrichment does not introduce systematic bias to genome characterization. SNP calling between samples when genotyping both de‐novo and with a reference genome are highly congruent (>98%) when specifying a minimum threshold of 20X stack depth to call genotypes. Relative genome coverage is also similar between MDA and gDNA, and allelic dropout is not observed. SNP concordance varies based on coverage threshold, with 95% concordance reached at ~12X coverage genotyping de‐novo and ~7X coverage genotyping with the reference genome. These results suggest that MDA may be a suitable solution for next‐generation molecular ecological studies when DNA quantity would otherwise be a limiting factor.     

An efficient method for measuring copy number variation applied to improvement of nematode resistance in soybean

Copy number variation (CNV) is implicated in important traits in multiple crop plants, but can be challenging to genotype using conventional methods. The Rhg1 locus of soybean, which confers resistance to soybean cyst nematode (SCN), is a CNV of multiple 31.2‐kb genomic units each containing four genes. Reliable, high‐throughput methods to quantify Rhg1 and other CNVs for selective breeding were developed. The CNV genotyping assay described here uses a homeologous gene copy within the paleopolyploid soybean genome to provide the internal control for a single‐tube TaqMan copy number assay. Using this assay, CNV in breeding populations can be tracked with high precision. We also show that extensive CNV exists within Fayette, a released, inbred SCN‐resistant soybean cultivar with a high copy number at Rhg1 derived from a single donor parent. Copy number at Rhg1 is therefore unstable within a released variety over a relatively small number of generations. Using this assay to select for individuals with altered copy number, plants were obtained with both increased copy number and increased SCN resistance relative to control plants. Thus, CNV genotyping technologies can be used as a new type of marker‐assisted selection to select for desirable traits in breeding populations, and to control for undesirable variation within cultivars.     

基因组拷贝数变异及其突变机理与人类疾病

拷贝数变异(Copy number variation,CNV)是由基因组发生重排而导致的,一般指长度为1 kb以上的基因组大片段的拷贝数增加或者减少,主要表现为亚显微水平的缺失和重复。CNV是基因组结构变异(Structural variation,SV)的重要组成部分。CNV位点的突变率远高于SNP(Single nucleotide polymorphism),是人类疾病的重要致病因素之一。目前,用来进行全基因组范围的CNV研究的方法有:基于芯片的比较基因组杂交技术(array-based comparative genomic hybridization,aCGH)、SNP分型芯片技术和新一代测序技术。CNV的形成机制有多种,并可分为DNA重组和DNA错误复制两大类。CNV可以导致呈孟德尔遗传的单基因病与罕见疾病,同时与复杂疾病也相关。其致病的可能机制有基因剂量效应、基因断裂、基因融合和位置效应等。对CNV的深入研究,可以使我们对人类基因组的构成、个体间的遗传差异、以及遗传致病因素有新的认识。     

Accessing single nucleotide polymorphisms in genomic DNA by direct multiplex polymerase chain reaction amplification on oligonucleotide microarrays

This study introduces a DNA microarray-based genotyping system for accessing single nucleotide polymorphisms (SNPs) directly from a genomic DNA sample. The described one-step approach combines multiplex amplification and allele-specific solid-phase PCR into an on-chip reaction platform. The multiplex amplification of genomic DNA and the genotyping reaction are both performed directly on the microarray in a single reaction. Oligonucleotides that interrogate single nucleotide positions within multiple genomic regions of interest are covalently tethered to a glass chip, allowing quick analysis of reaction products by fluorescence scanning. Due to a fourfold SNP detection approach employing simultaneous probing of sense and antisense strand information, genotypes can be automatically assigned and validated using a simple computer algorithm. We used the described procedure for parallel genotyping of 10 different polymorphisms in a single reaction and successfully analyzed more than 100 human DNA samples. More than 99% of genotype data were in agreement with data obtained in control experiments with allele-specific oligonucleotide hybridization and capillary sequencing. Our results suggest that this approach might constitute a powerful tool for the analysis of genetic variation.     

Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data

Background

Genomic deletions and duplications are important in the pathogenesis of diseases, such as cancer and mental retardation, and have recently been shown to occur frequently in unaffected individuals as polymorphisms. Affymetrix GeneChip whole genome sampling analysis (WGSA) combined with 100 K single nucleotide polymorphism (SNP) genotyping arrays is one of several microarray-based approaches that are now being used to detect such structural genomic changes. The popularity of this technology and its associated open source data format have resulted in the development of an increasing number of software packages for the analysis of copy number changes using these SNP arrays.

Results

We evaluated four publicly available software packages for high throughput copy number analysis using synthetic and empirical 100 K SNP array data sets, the latter obtained from 107 mental retardation (MR) patients and their unaffected parents and siblings. We evaluated the software with regards to overall suitability for high-throughput 100 K SNP array data analysis, as well as effectiveness of normalization, scaling with various reference sets and feature extraction, as well as true and false positive rates of genomic copy number variant (CNV) detection.

Conclusion

We observed considerable variation among the numbers and types of candidate CNVs detected by different analysis approaches, and found that multiple programs were needed to find all real aberrations in our test set. The frequency of false positive deletions was substantial, but could be greatly reduced by using the SNP genotype information to confirm loss of heterozygosity.