Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms.
paper
Cited
Public
- Authors
- Diskin, Sharon J; Li, Mingyao; Hou, Cuiping; Yang, Shuzhang; Glessner, Joseph; Hakonarson, Hakon; Bucan, Maja; Maris, John M; Wang, Kai
- Year
- 2008
- Journal
- Nucleic acids research
- PMID
- 18784189
- DOI
- 10.1093/nar/gkn556
- PMCID
- PMC2577347
Whole-genome microarrays with large-insert clones designed to determine DNA copy number often show variation in hybridization intensity that is related to the genomic position of the clones. We found these 'genomic waves' to be present in Illumina and Affymetrix SNP genotyping arrays, confirming that they are not platform-specific. The causes of genomic waves are not well-understood, and they may prevent accurate inference of copy number variations (CNVs). By measuring DNA concentration for 1444 samples and by genotyping the same sample multiple times with varying DNA quantity, we demonstrated that DNA quantity correlates with the magnitude of waves. We further showed that wavy signal patterns correlate best with GC content, among multiple genomic features considered. To measure the magnitude of waves, we proposed a GC-wave factor (GCWF) measure, which is a reliable predictor of DNA quantity (correlation coefficient = 0.994 based on samples with serial dilution). Finally, we developed a computational approach by fitting regression models with GC content included as a predictor variable, and we show that this approach improves the accuracy of CNV detection. With the wide application of whole-genome SNP genotyping techniques, our wave adjustment method will be important for taking full advantage of genotyped samples for CNV analysis.
Genomic wave is not platform-specific. An illustration of four representative examples showing the LRR values for chromosome 11 in the Affymetrix 250K NSP (from Affymetrix Mapping 500K array sets) and genome-wide 6.0 arrays, as well as the Illumina HumanHap550 and HumanHap1M arrays. Four different DNA samples were genotyped by these four arrays. In all cases, we observe similar shapes of wavy patterns with identical or opposite peaks and troughs. This indicates that genomic wave is an intrinsic property of the human genome, regardless of the technical platforms.
Genomic wave is correlated with GC content. Analysis of the correlation between GC percentage and median values of LRR in nonoverlapping windows across the genome, using 10, 100 kb and 1 Mb window sizes, respectively. (A) A sample genotyped by the Illumina HumanHap550 array is chosen and the signal pattern on chromosome 11 is shown. (B–D) We observe increasingly higher correlations between these two measures with larger sliding window sizes. The correlation coefficients for panels (B)–(D) are 0.70, 0.85 and 0.96, respectively. (E) A sample genotyped by the Illumina HumanHap550 array that has opposite peaks and troughs as (A) is chosen and the signal pattern on chromosome 11 is shown. (F–G) We observe increasingly higher correlations between these two measures with larger sliding window sizes.
The signal intensity is highly correlated with GC content in sliding windows in chromosome 11. The median LRR values for 1 Mb sliding windows in two samples (one with negative wave and one with positive wave) are plotted against several genomic features, including GC content, segmental duplication, gene content, exon content, simple repeats and conserved region.
Genomic wave is correlated with DNA quantity, but not quality. (A) Plot of GCWF measure against 260/280 ratio for DNA of 1444 neuroblastoma patients (Pearson correlation coefficient = 0.0918). (B) Gel electrophoresis assay for 750 ng DNA from 18 genotyped samples (six without waves, six with positive waves and six with negative waves) shows no evidence of DNA degradation, which would appear as smears rather than clear bands (see Supplementary Figure 2 for examples). The largest size marker for the 100-bp ladder lane is 2072 bp, while the largest size marker for the 1-kb ladder lane is 12 kb. (C) Plot of GCWF against total quantity of DNA for 1444 samples (Pearson correlation coefficient = 0.4371). Samples with an initial estimated concentration > 100 ng/µl were diluted to 75 ng/µl, explaining the clustering at 1125 ng. (D) Plot of DNA quantity versus GCWF for serial dilutions of DNA from a single sample.
A comparison of signal intensities along chromosome 11 for five duplicated samples with different DNA quantities genotyped by the Illumina HumanHap550 arrays. The LRR signal intensity shows wavy patterns when the DNA quantity deviates from the recommended quantity (750 ng). The directionality of the waves is reversed when increasing amounts of DNA is used for genotyping.
DNA quantity and waves impact CNV calling. CNV calling results from the cnvPartition algorithm, and the PennCNV algorithm without and with signal adjustment, as implemented in the BeadStudio software, on five duplicated DNA samples with different quantities. A 5-SNP threshold was used in PennCNV so that the number of calls was comparable to cnvPartition. The color and the thickness of the bars represent the copy number and the size of the CNV calls, respectively. DNA quantity and signal intensity waves severely affect the accuracy of CNV calling; however, after signal adjustment, higher specificity and higher concordance rate in CNV calls are achieved.
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| Chromosome dynamic changes in two cultured Chinese human embryonic stem cell lines: single nucleotide polymorphism, copy number variation and loss of heterozygosity. | Chen XM et al. | — | 2012 | → |
| Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data. | Mosén-Ansorena D et al. | — | 2012 | → |
| Copy number variation in patients with cervical artery dissection. | Grond-Ginsbach C et al. | — | 2012 | → |
| Copy number variations in 6q14.1 and 5q13.2 are associated with alcohol dependence. | Lin P et al. | — | 2012 | → |
| Detectable clonal mosaicism and its relationship to aging and cancer. | Jacobs KB et al. | — | 2012 | → |
| Detectable clonal mosaicism from birth to old age and its relationship to cancer. | Laurie CC et al. | — | 2012 | → |
| EGFR gene variants are associated with specific somatic aberrations in glioma. | Wibom C et al. | — | 2012 | → |
| ERK inhibition overcomes acquired resistance to MEK inhibitors. | Hatzivassiliou G et al. | — | 2012 | → |
| Fast detection of de novo copy number variants from SNP arrays for case-parent trios. | Scharpf RB et al. | — | 2012 | → |
| Fine mapping of copy number variations on two cattle genome assemblies using high density SNP array. | Hou Y et al. | — | 2012 | → |
| Genetic association studies of copy-number variation: should assignment of copy number states precede testing? | Breheny P et al. | — | 2012 | → |
| Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events. | Liu J et al. | — | 2012 | → |
| Genome-wide survey of large rare copy number variants in Alzheimer's disease among Caribbean hispanics. | Ghani M et al. | — | 2012 | → |
| Hybridization and amplification rate correction for affymetrix SNP arrays. | Wang Q et al. | — | 2012 | → |
| Inheritance model introduces differential bias in CNV calls between parents and offspring. | Kim S et al. | — | 2012 | → |
| Phosphoinositide 3-kinase (PI3K) pathway alterations are associated with histologic subtypes and are predictive of sensitivity to PI3K inhibitors in lung cancer preclinical models. | Spoerke JM et al. | — | 2012 | → |
| Rare genomic deletions and duplications and their role in neurodevelopmental disorders. | Glessner JT et al. | — | 2012 | → |
| Reconstructing DNA copy number by joint segmentation of multiple sequences. | Zhang Z et al. | — | 2012 | → |
| Renal cell neoplasms contain shared tumor type-specific copy number variations. | Krill-Burger JM et al. | — | 2012 | → |
| Role of common and rare APP DNA sequence variants in Alzheimer disease. | Hooli BV et al. | — | 2012 | → |
| Transcriptome profiling of UPF3B/NMD-deficient lymphoblastoid cells from patients with various forms of intellectual disability. | Nguyen LS et al. | — | 2012 | → |
| Accurate and exact CNV identification from targeted high-throughput sequence data. | Nord AS et al. | — | 2011 | → |
| A novel signal processing approach for the detection of copy number variations in the human genome. | Stamoulis C et al. | — | 2011 | → |
| A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples. | Lisovich A et al. | — | 2011 | → |
| A pipeline for copy number variation detection based on principal component analysis. | Chen J et al. | — | 2011 | → |
| Characterization of autosomal copy-number variation in African Americans: the HyperGEN Study. | Wineinger NE et al. | — | 2011 | → |
| CNV-WebStore: online CNV analysis, storage and interpretation. | Vandeweyer G et al. | — | 2011 | → |
| Copy number imbalances in blood and hair in monozygotic twins discordant for amyotrophic lateral sclerosis. | Pamphlett R et al. | — | 2011 | → |
| Copy number variation accuracy in genome-wide association studies. | Lin P et al. | — | 2011 | → |
| Copy number variations and primary open-angle glaucoma. | Davis LK et al. | — | 2011 | → |
| Exome sequencing and unrelated findings in the context of complex disease research: ethical and clinical implications. | Lyon GJ et al. | — | 2011 | → |
| Expanding the range of ZNF804A variants conferring risk of psychosis. | Steinberg S et al. | — | 2011 | → |
| Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder. | Elia J et al. | — | 2011 | → |
| Genomic characteristics of cattle copy number variations. | Hou Y et al. | — | 2011 | → |
| Genomic Copy Number Analysis in Alzheimer's Disease and Mild Cognitive Impairment: An ADNI Study. | Swaminathan S et al. | — | 2011 | → |
| GPHMM: an integrated hidden Markov model for identification of copy number alteration and loss of heterozygosity in complex tumor samples using whole genome SNP arrays. | Li A et al. | — | 2011 | → |
| Hot topic: performance of bovine high-density genotyping platforms in Holsteins and Jerseys. | Rincon G et al. | — | 2011 | → |
| Identification of a common variant affecting human episodic memory performance using a pooled genome-wide association approach: a case study of disease gene identification. | Pawlowski TL et al. | — | 2011 | → |
| Looking for differences in copy number between blood and brain in sporadic amyotrophic lateral sclerosis. | Pamphlett R et al. | — | 2011 | → |
| Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. | Jacquemont S et al. | — | 2011 | → |
| Performance assessment of copy number microarray platforms using a spike-in experiment. | Halper-Stromberg E et al. | — | 2011 | → |
| Phenotype mining in CNV carriers from a population cohort. | Pietiläinen OP et al. | — | 2011 | → |
| Preprocessing and downstream analysis of microarray DNA copy number profiles. | van de Wiel MA et al. | — | 2011 | → |
| Single-cell copy number variation detection. | Cheng J et al. | — | 2011 | → |
| Software comparison for evaluating genomic copy number variation for Affymetrix 6.0 SNP array platform. | Eckel-Passow JE et al. | — | 2011 | → |
| Technological issues and experimental design of gene association studies. | Distefano JK et al. | — | 2011 | → |
| Using case-parent trios to look for rare de novo genetic variants in adult-onset neurodegenerative diseases. | Pamphlett R et al. | — | 2011 | → |
| A genome-wide study reveals copy number variants exclusive to childhood obesity cases. | Glessner JT et al. | — | 2010 | → |
| Assessing Population Level Genetic Instability via Moving Average. | McDaniel S et al. | — | 2010 | → |
| A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. | Yau C et al. | — | 2010 | → |
| Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays. | Guo B et al. | — | 2010 | → |
| CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. | Zhang Q et al. | — | 2010 | → |
| CNAnova: a new approach for finding recurrent copy number abnormalities in cancer SNP microarray data. | Ivakhno S et al. | — | 2010 | → |
| Correction of Copy Number Variation Data Using Principal Component Analysis. | Chen J et al. | — | 2010 | → |
| Duplication of the SLIT3 locus on 5q35.1 predisposes to major depressive disorder. | Glessner JT et al. | — | 2010 | → |
| Integration of transcript expression, copy number and LOH analysis of infiltrating ductal carcinoma of the breast. | Hawthorn L et al. | — | 2010 | → |
| MixHMM: inferring copy number variation and allelic imbalance using SNP arrays and tumor samples mixed with stromal cells. | Liu Z et al. | — | 2010 | → |
| RECONSTRUCTING DNA COPY NUMBER BY PENALIZED ESTIMATION AND IMPUTATION. | Zhang Z et al. | — | 2010 | → |
| Strong synaptic transmission impact by copy number variations in schizophrenia. | Glessner JT et al. | — | 2010 | → |
| waviCGH: a web application for the analysis and visualization of genomic copy number alterations. | Carro A et al. | — | 2010 | → |
| A Bayesian segmentation approach to ascertain copy number variations at the population level. | Wu LY et al. | — | 2009 | → |
| Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. | Glessner JT et al. | — | 2009 | → |
| Copy number variation at 1q21.1 associated with neuroblastoma. | Diskin SJ et al. | — | 2009 | → |
| Genomic landscape of a three-generation pedigree segregating affective disorder. | Yang S et al. | — | 2009 | → |
| Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation. | Wan L et al. | — | 2009 | → |
| Joint estimation of copy number variation and reference intensities on multiple DNA arrays using GADA. | Pique-Regi R et al. | — | 2009 | → |
| Markov Models for inferring copy number variations from genotype data on Illumina platforms. | Wang H et al. | — | 2009 | → |
| Microduplications of 16p11.2 are associated with schizophrenia. | McCarthy SE et al. | — | 2009 | → |