"GenotypeColour™": colour visualisation of SNPs and CNVs

<p>Abstract</p> <p>Background</p> <p>The volume of data available on genetic variations has increased considerably with the recent development of high-density, single-nucleotide polymorphism (SNP) arrays. Several software programs have been developed to assist researchers in the analysis of this huge amount of data, but few can rely upon a whole genome variability visualisation system that could help data interpretation.</p> <p>Results</p> <p>We have developed <it>GenotypeColour™ </it>as a rapid user-friendly tool able to upload, visualise and compare the huge amounts of data produced by Affymetrix Human Mapping GeneChips without losing the overall view of the data.</p> <p>Some features of <it>GenotypeColour™ </it>include visualising the entire genome variability in a single screenshot for one or more samples, the simultaneous display of the genotype and Copy Number state for thousands of SNPs, and the comparison of large amounts of samples by producing "consensus" images displaying regions of complete or partial identity. The software is also useful for genotype analysis of trios and to show regions of potential uniparental disomy (UPD). All information can then be exported in a tabular format for analysis with dedicated software. At present, the software can handle data from 10 K, 100 K, 250 K, 5.0 and 6.0 Affymetrix chips.</p> <p>Conclusion</p> <p>We have created a software that offers a new way of displaying and comparing SNP and CNV genomic data. The software is available free at <url>http://www.med.unibs.it/~barlati/GenotypeColour</url> and is especially useful for the analysis of multiple samples.</p

Genotyping and annotation of Affymetrix SNP arrays

In this paper we develop a new method for genotyping Affymetrix single nucleotide polymorphism (SNP) array. The method is based on (i) using multiple arrays at the same time to determine the genotypes and (ii) a model that relates intensities of individual SNPs to each other. The latter point allows us to annotate SNPs that have poor performance, either because of poor experimental conditions or because for one of the alleles the probes do not behave in a dose–response manner. Generally, our method agrees well with a method developed by Affymetrix. When both methods make a call they agree in 99.25% (using standard settings) of the cases, using a sample of 113 Affymetrix 10k SNP arrays. In the majority of cases where the two methods disagree, our method makes a genotype call, whereas the method by Affymetrix makes a no call, i.e. the genotype of the SNP is not determined. By visualization it is indicated that our method is likely to be correct in majority of these cases. In addition, we demonstrate that our method produces more SNPs that are in concordance with Hardy–Weinberg equilibrium than the method by Affymetrix. Finally, we have validated our method on HapMap data and shown that the performance of our method is comparable to other methods

Next‐generation sequencing in two cases of de novo acute basophilic leukaemia

Acute basophilic leukaemia (ABL) is a rare subtype of acute myeloid leukaemia (AML); therefore, few data are available about its biology. Herein, we analysed two ABL patients using flow cytometry and next-generation sequencing (NGS). Two cell populations were detected by flow cytometry in both patients. In Case no. 1, blasts (CD34⁺, CD203c⁻, CD117⁺, CD123dim⁺) and basophils (CD34⁻, CD203c⁺, CD117±, CD123⁺) were identified, both of which were found by NGS to harbour the 17p deletion and have loss of heterozygosity of TP53. In Case no. 2, blasts (CD33⁺, CD34⁺, CD123⁻) and basophils (CD33⁺, CD34⁺, CD123⁺) were identified. NGS detected NPM1 mutations in either blasts or basophils, and TET2 in both. These data suggest an overlap of the mutational landscape of ABL and AML, including TP53 and TET2 mutations. Moreover, additional mutations or epigenetic factors may contribute for the differentiation into basophilic blasts

Free energy of DNA duplex formation on short oligonucleotide microarrays

DNA/DNA duplex formation is the basic mechanism that is used in genome tiling arrays and SNP arrays manufactured by Affymetrix. However, detailed knowledge of the physical process is still lacking. In this study, we show a free energy analysis of DNA/DNA duplex formation these arrays based on the positional-dependent nearest-neighbor (PDNN) model, which was developed previously for describing DNA/RNA duplex formation on expression microarrays. Our results showed that the two ends of a probe contribute less to the stability of the duplexes and that there is a microarray surface effect on binding affinities. We also showed that free energy cost of a single mismatch depends on the bases adjacent to the mismatch site and obtained a comprehensive table of the cost of a single mismatch under all possible combination of adjacent bases. The mismatch costs were found to be correlated with those determined in aqueous solution. We further demonstrate that the DNA copy number estimated from the SNP array correlates negatively with the target length; this is presumably caused by inefficient PCR amplification for long fragments. These results provide important insights into the molecular mechanisms of microarray technology and have implications for microarray design and the interpretation of observed data

SiDCoN: A Tool to Aid Scoring of DNA Copy Number Changes in SNP Chip Data

The recent application of genome-wide, single nucleotide polymorphism (SNP) microarrays to investigate DNA copy number aberrations in cancer has provided unparalleled sensitivity for identifying genomic changes. In some instances the complexity of these changes makes them difficult to interpret, particularly when tumour samples are contaminated with normal (stromal) tissue. Current automated scoring algorithms require considerable manual data checking and correction, especially when assessing uncultured tumour specimens. To address these limitations we have developed a visual tool to aid in the analysis of DNA copy number data. Simulated DNA Copy Number (SiDCoN) is a spreadsheet-based application designed to simulate the appearance of B-allele and logR plots for all known types of tumour DNA copy number changes, in the presence or absence of stromal contamination. The system allows the user to determine the level of stromal contamination, as well as specify up to 3 different DNA copy number aberrations for up to 5000 data points (representing individual SNPs). This allows users great flexibility to assess simple or complex DNA copy number combinations. We demonstrate how this utility can be used to estimate the level of stromal contamination within tumour samples and its application in deciphering the complex heterogeneous copy number changes we have observed in a series of tumours. We believe this tool will prove useful to others working in the area, both as a training tool, and to aid in the interpretation of complex copy number changes

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

ESTIMATING GENOME-WIDE COPY NUMBER USING ALLELE SPECIFIC MIXTURE MODELS

Genomic changes such as copy number alterations are thought to be one of the major underlying causes of human phenotypic variation among normal and disease subjects [23,11,25,26,5,4,7,18]. These include chromosomal regions with so-called copy number alterations: instead of the expected two copies, a section of the chromosome for a particular individual may have zero copies (homozygous deletion), one copy (hemizygous deletions), or more than two copies (amplifications). The canonical example is Down syndrome which is caused by an extra copy of chromosome 21. Identification of such abnormalities in smaller regions has been of great interest, because it is believed to be an underlying cause of cancer. More than one decade ago comparative genomic hybridization (CGH)technology was developed to detect copy number changes in a high-throughput fashion. However, this technology only provides a 10 MB resolution which limits the ability to detect copy number alterations spanning small regions. It is widely believed that a copy number alteration as small as one base can have significant downstream effects, thus microarray manufacturers have developed technologies that provide much higher resolution. Unfortunately, strong probe effects and variation introduced by sample preparation procedures have made single-point copy number estimates too imprecise to be useful. CGH arrays use a two-color hybridization, usually comparing a sample of interest to a reference sample, which to some degree removes the probe effect. However, the resolution is not nearly high enough to provide single-point copy number estimates. Various groups have proposed statistical procedures that pool data from neighboring locations to successfully improve precision. However, these procedure need to average across relatively large regions to work effectively thus greatly reducing the resolution. Recently, regression-type models that account for probe-effect have been proposed and appear to improve accuracy as well as precision. In this paper, we propose a mixture model solution specifically designed for single-point estimation, that provides various advantages over the existing methodology. We use a 314 sample database, constructed with public datasets, to motivate and fit models for the conditional distribution of the observed intensities given allele specific copy numbers. With the estimated models in place we can compute posterior probabilities that provide a useful prediction rule as well as a confidence measure for each call. Software to implement this procedure will be available in the Bioconductor oligo packagehttp://www.bioconductor.org)

Combined landscape of single-nucleotide variants and copy number alterations in clonal hematopoiesis

クローン性造血の臨床予後への影響を解明 --遺伝子変異とコピー数異常の統合的な知見--. 京都大学プレスリリース. 2021-07-09.Clonal hematopoiesis (CH) in apparently healthy individuals is implicated in the development of hematological malignancies (HM) and cardiovascular diseases. Previous studies of CH analyzed either single-nucleotide variants and indels (SNVs/indels) or copy number alterations (CNAs), but not both. Here, using a combination of targeted sequencing of 23 CH-related genes and array-based CNA detection of blood-derived DNA, we have delineated the landscape of CH-related SNVs/indels and CNAs in 11, 234 individuals without HM from the BioBank Japan cohort, including 672 individuals with subsequent HM development, and studied the effects of these somatic alterations on mortality from HM and cardiovascular disease, as well as on hematological and cardiovascular phenotypes. The total number of both types of CH-related lesions and their clone size positively correlated with blood count abnormalities and mortality from HM. CH-related SNVs/indels and CNAs exhibited statistically significant co-occurrence in the same individuals. In particular, co-occurrence of SNVs/indels and CNAs affecting DNMT3A, TET2, JAK2 and TP53 resulted in biallelic alterations of these genes and was associated with higher HM mortality. Co-occurrence of SNVs/indels and CNAs also modulated risks for cardiovascular mortality. These findings highlight the importance of detecting both SNVs/indels and CNAs in the evaluation of CH

Fusion partner–specific mutation profiles and KRAS mutations as adverse prognostic factors in MLL-rearranged AML

急性骨髄性白血病の予後を予測する新規マーカーを発見 --リスクに応じた適切な治療につながる可能性--. 京都大学プレスリリース. 2020-10-02.Mixed-lineage leukemia (MLL) gene rearrangements are among the most frequent chromosomal abnormalities in acute myeloid leukemia (AML). MLL fusion patterns are associated with the patient’s prognosis; however, their relationship with driver mutations is unclear. We conducted sequence analyses of 338 genes in pediatric patients with MLL-rearranged (MLL-r) AML (n = 56; JPLSG AML-05 study) alongside data from the TARGET study’s pediatric cohorts with MLL-r AML (n = 104), non–MLL-r AML (n = 581), and adult MLL-r AML (n = 81). KRAS mutations were most frequent in pediatric patients with high-risk MLL fusions (MLL-MLLLT10, MLL-MLLT4, and MLL-MLLT1). Pediatric patients with MLL-r AML (n = 160) and a KRAS mutation (KRAS-MT) had a significantly worse prognosis than those without a KRAS mutation (KRAS-WT) (5-year event-free survival [EFS]: 51.8% vs 18.3%, P < .0001; 5-year overall survival [OS]: 67.3% vs 44.3%, P = .003). The adverse prognostic impact of KRAS mutations was confirmed in adult MLL-r AML. KRAS mutations were associated with adverse prognoses in pediatric patients with both high-risk (MLLT10+MLLT4+MLLT1; n = 60) and intermediate-to-low–risk (MLLT3+ELL+others; n = 100) MLL fusions. The prognosis did not differ significantly between patients with non–MLL-r AML with KRAS-WT or KRAS-MT. Multivariate analysis showed the presence of a KRAS mutation to be an independent prognostic factor for EFS (hazard ratio [HR], 2.21; 95% confidence interval [CI], 1.35-3.59; P = .002) and OS (HR, 1.85; 95% CI, 1.01-3.31; P = .045) in MLL-r AML. The mutation is a distinct adverse prognostic factor in MLL-r AML, regardless of risk subgroup, and is potentially useful for accurate treatment stratification. This trial was registered at the UMIN (University Hospital Medical Information Network) Clinical Trials Registry (UMIN-CTR; http://www.umin.ac.jp/ctr/index.htm) as #UMIN000000511