Extended homozygosity is not usually due to cytogenetic abnormality

<p>Abstract</p> <p>Background</p> <p>Previous studies have reported frequent stretches of homozygosity in human subjects but have failed to clarify whether these are due to cytogenetic abnormalities or to autozygosity.</p> <p>Methods</p> <p>Trios which had been typed for closely spaced SNPs spanning the genome were studied. Stretches of extended homozygosity were identified in the child members, as were occasions on which the child had been genotyped as not inheriting one parental allele. The number of times such transmission errors occurred within regions of extended homozygosity was compared with the chance expectation.</p> <p>Results</p> <p>Transmission errors occurred more rarely in regions of extended homozygosity than would be expected by chance.</p> <p>Discussion</p> <p>Regions of extended homozygosity are not generally due to cytogenetic abnormalities such as uniparental isodisomy. They reflect the Mendelian inheritance of haplotypes from a common ancestor. This may have implications for mapping disease genes.</p

MMBGX: a method for estimating expression at the isoform level and detecting differential splicing using whole-transcript Affymetrix arrays

Affymetrix has recently developed whole-transcript GeneChips—‘Gene’ and ‘Exon’ arrays—which interrogate exons along the length of each gene. Although each probe on these arrays is intended to hybridize perfectly to only one transcriptional target, many probes match multiple transcripts located in different parts of the genome or alternative isoforms of the same gene. Existing statistical methods for estimating expression do not take this into account and are thus prone to producing inflated estimates. We propose a method, Multi-Mapping Bayesian Gene eXpression (MMBGX), which disaggregates the signal at ‘multi-match’ probes. When applied to Gene arrays, MMBGX removes the upward bias of gene-level expression estimates. When applied to Exon arrays, it can further disaggregate the signal between alternative transcripts of the same gene, providing expression estimates of individual splice variants. We demonstrate the performance of MMBGX on simulated data and a tissue mixture data set. We then show that MMBGX can estimate the expression of alternative isoforms within one experimental condition, confirming our results by RT-PCR. Finally, we show that our method for detecting differential splicing has a lower error rate than standard exon-level approaches on a previously validated colon cancer data set

Implementation of exon arrays: alternative splicing during T-cell proliferation as determined by whole genome analysis

<p>Abstract</p> <p>Background</p> <p>The contribution of alternative splicing and isoform expression to cellular response is emerging as an area of considerable interest, and the newly developed exon arrays allow for systematic study of these processes. We use this pilot study to report on the feasibility of exon array implementation looking to replace the 3' <it>in vitro </it>transcription expression arrays in our laboratory.</p> <p>One of the most widely studied models of cellular response is T-cell activation from exogenous stimulation. Microarray studies have contributed to our understanding of key pathways activated during T-cell stimulation. We use this system to examine whole genome transcription and alternate exon usage events that are regulated during lymphocyte proliferation in an attempt to evaluate the exon arrays.</p> <p>Results</p> <p>Peripheral blood mononuclear cells form healthy donors were activated using phytohemagglutinin, IL2 and ionomycin and harvested at 5 points over a 7 day period. Flow cytometry measured cell cycle events and the Affymetrix exon array platform was used to identify the gene expression and alternate exon usage changes. Gene expression changes were noted in a total of 2105 transcripts, and alternate exon usage identified in 472 transcript clusters. There was an overlap of 263 transcripts which showed both differential expression and alternate exon usage over time. Gene ontology enrichment analysis showed a broader range of biological changes in biological processes for the differentially expressed genes, which include cell cycle, cell division, cell proliferation, chromosome segregation, cell death, component organization and biogenesis and metabolic process ontologies. The alternate exon usage ontological enrichments are in metabolism and component organization and biogenesis. We focus on alternate exon usage changes in the transcripts of the spliceosome complex. The real-time PCR validation rates were 86% for transcript expression and 71% for alternate exon usage.</p> <p>Conclusions</p> <p>This study illustrates that the Exon array technology has the potential to provide information on both transcript expression and isoform usage, with very little increase in expense.</p

Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips

BACKGROUND: Affymetrix GeneChip technology enables the parallel observations of tens of thousands of genes. It is important that the probe set annotations are reliable so that biological inferences can be made about genes which undergo differential expression. Probe sets representing the same gene might be expected to show similar fold changes/z-scores, however this is in fact not the case. RESULTS: We have made a case study of the mouse Surf4, chosen because it is a gene that was reported to be represented by the same eight probe sets on the MOE430A array by both Affymetrix and Bioconductor in early 2004. Only five of the probe sets actually detect Surf4 transcripts. Two of the probe sets detect splice variants of Surf2. We have also studied the expression changes of the eight probe sets in a public-domain microarray experiment. The transcripts for Surf4 are correlated in time, and similarly the transcripts for Surf2 are also correlated in time. However, the transcripts for Surf4 and Surf2 are not correlated. This proof of principle shows that observations of expression can be used to confirm, or otherwise, annotation discrepancies. We have also investigated groups of probe sets on the RAE230A array that are assigned to the same LocusID, but which show large variances in differential expression in any one of three different experiments on rat. The probe set groups with high variances are found to represent cases of alternative splicing, use of alternative poly(A) signals, or incorrect annotations. CONCLUSION: Our results indicate that some probe sets should not be considered as unique measures of transcription, because the individual probes map to more than one transcript dependent upon the biological condition. Our results highlight the need for care when assessing whether groups of probe sets all measure the same transcript

A single-array preprocessing method for estimating full-resolution raw copy numbers from all Affymetrix genotyping arrays including GenomeWideSNP 5 & 6

Motivation: High-resolution copy-number (CN) analysis has in recent years gained much attention, not only for the purpose of identifying CN aberrations associated with a certain phenotype, but also for identifying CN polymorphisms. In order for such studies to be successful and cost effective, the statistical methods have to be optimized. We propose a single-array preprocessing method for estimating full-resolution total CNs. It is applicable to all Affymetrix genotyping arrays, including the recent ones that also contain non-polymorphic probes. A reference signal is only needed at the last step when calculating relative CNs

Non-linear analysis of GeneChip arrays

The application of microarray hybridization theory to Affymetrix GeneChip data has been a recent focus for data analysts. It has been shown that the hyperbolic Langmuir isotherm captures the shape of the signal response to concentration of Affymetrix GeneChips. We demonstrate that existing linear fit methods for extracting gene expression measures are not well adapted for the effect of saturation resulting from surface adsorption processes. In contrast to the most popular methods, we fit background and concentration parameters within a single global fitting routine instead of estimating the background before obtaining gene expression measures. We describe a non-linear multi-chip model of the perfect match signal that effectively allows for the separation of specific and non-specific components of the microarray signal and avoids saturation bias in the high-intensity range. Multimodel inference, incorporated within the fitting routine, allows a quantitative selection of the model that best describes the observed data. The performance of this method is evaluated on publicly available datasets, and comparisons to popular algorithms are presented

"Hook"-calibration of GeneChip-microarrays: Chip characteristics and expression measures

<p>Abstract</p> <p>Background</p> <p>Microarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.</p> <p>Results</p> <p>In this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.</p> <p>Conclusion</p> <p>The single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.</p

"Hook"-calibration of GeneChip-microarrays: Theory and algorithm

Abstract Background: The improvement of microarray calibration methods is an essential prerequisite for quantitative expression analysis. This issue requires the formulation of an appropriate model describing the basic relationship between the probe intensity and the specific transcript concentration in a complex environment of competing interactions, the estimation of the magnitude these effects and their correction using the intensity information of a given chip and, finally the development of practicable algorithms which judge the quality of a particular hybridization and estimate the expression degree from the intensity values. Results: We present the so-called hook-calibration method which co-processes the log-difference (delta) and -sum (sigma) of the perfect match (PM) and mismatch (MM) probe-intensities. The MM probes are utilized as an internal reference which is subjected to the same hybridization law as the PM, however with modified characteristics. After sequence-specific affinity correction the method fits the Langmuir-adsorption model to the smoothed delta-versus-sigma plot. The geometrical dimensions of this so-called hook-curve characterize the particular hybridization in terms of simple geometric parameters which provide information about the mean non-specific background intensity, the saturation value, the mean PM/MM-sensitivity gain and the fraction of absent probes. This graphical summary spans a metrics system for expression estimates in natural units such as the mean binding constants and the occupancy of the probe spots. The method is single-chip based, i.e. it separately uses the intensities for each selected chip. Conclusion: The hook-method corrects the raw intensities for the non-specific background hybridization in a sequence-specific manner, for the potential saturation of the probe-spots with bound transcripts and for the sequence-specific binding of specific transcripts. The obtained chip characteristics in combination with the sensitivity corrected probe-intensity values provide expression estimates scaled in natural units which are given by the binding constants of the particular hybridization.</p

Analysis of the early immune response to infection by infectious bursal disease virus in chickens differing in their resistance to the disease

Chicken whole-genome gene expression arrays were used to analyze the host response to infection by infectious bursal disease virus (IBDV). Spleen and bursal tissue were examined from control and infected birds at 2, 3, and 4 days postinfection from two lines that differ in their resistance to IBDV infection. The host response was evaluated over this period, and differences between susceptible and resistant chicken lines were examined. Antiviral genes, including IFNA, IFNG, MX1, IFITM1, IFITM3, and IFITM5, were upregulated in response to infection. Evaluation of this gene expression data allowed us to predict several genes as candidates for involvement in resistance to IBDV. © 2015, American Society for Microbiology

Cross-hybridization modeling on Affymetrix exon arrays

Motivation: Microarray designs have become increasingly probe-rich, enabling targeting of specific features, such as individual exons or single nucleotide polymorphisms. These arrays have the potential to achieve quantitative high-throughput estimates of transcript abundances, but currently these estimates are affected by biases due to cross-hybridization, in which probes hybridize to off-target transcripts