Project description:Structural variants can lead to an alteration of gene expression which may be associated with disease worsening. In our study we attempted to describe expression changes associated with the presence of extensive genomic rearrangements in chronic lymphocytic leukemia. Overall design: We used Affymetrix microarrays to identify genomic rearrangements in chronic lymphocytic leukemia. Peripheral blood samples from 10 leukemic patients were used for genomic DNA extraction. Copy number analysis was performed and obtained data were correlated with expression profiles of these cases.
Project description:Structural variants can lead to an alteration of gene expression which may be associated with disease worsening. In our study we attempted to describe expression changes associated with the presence of extensive genomic rearrangements in chronic lymphocytic leukemia. We used microarrays for establishing an algorithm for identification of unique expression profiles associated with extensive genomic rearrangements. Overall design: Peripheral blood samples from leukemic patients were used for RNA extraction. Gene expression profiles were correlated to chromosomal abnormalities identified in the cohort.
Project description:Structural variants can lead to an alteration of gene expression which may be associated with disease worsening. In our study we attempted to describe expression changes associated with the presence of extensive genomic rearrangements in chronic lymphocytic leukemia. Overall design: Peripheral blood samples from leukemic patients were used for RNA extraction. Gene expression profiles were correlated to chromosomal abnormalities identified in the cohort.
Project description:BACKGROUND: Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets. RESULTS: Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs. CONCLUSION: Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.
Project description:<h4>Motivation</h4>Genome-wide association studies (GWAS) generate relationships between hundreds of thousands of single nucleotide polymorphisms (SNPs) and complex phenotypes. The contribution of the traditionally overlooked copy number variations (CNVs) to complex traits is also being actively studied. To facilitate the interpretation of the data and the designing of follow-up experimental validations, we have developed a database that enables the sensible prioritization of these variants by combining several approaches, involving not only publicly available physical and functional annotations but also multilocus linkage disequilibrium (LD) annotations as well as annotations of expression quantitative trait loci (eQTLs).<h4>Results</h4>For each SNP, the SCAN database provides: (i) summary information from eQTL mapping of HapMap SNPs to gene expression (evaluated by the Affymetrix exon array) in the full set of HapMap CEU (Caucasians from UT, USA) and YRI (Yoruba people from Ibadan, Nigeria) samples; (ii) LD information, in the case of a HapMap SNP, including what genes have variation in strong LD (pairwise or multilocus LD) with the variant and how well the SNP is covered by different high-throughput platforms; (iii) summary information available from public databases (e.g. physical and functional annotations); and (iv) summary information from other GWAS. For each gene, SCAN provides annotations on: (i) eQTLs for the gene (both local and distant SNPs) and (ii) the coverage of all variants in the HapMap at that gene on each high-throughput platform. For each genomic region, SCAN provides annotations on: (i) physical and functional annotations of all SNPs, genes and known CNVs within the region and (ii) all genes regulated by the eQTLs within the region.<h4>Availability</h4>http://www.scandb.org.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.
Project description:Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer in recent years has been dominated by the use of genome-wide technologies, such as single nucleotide polymorphism (SNP)-arrays, with a significant focus on single nucleotide variants. To date, these large datasets have been underutilised for generating genome-wide CNV profiles despite offering a massive resource for assessing the contribution of these structural variants to breast cancer risk. Technical challenges remain in determining the location and distribution of CNVs across the human genome due to the accuracy of computational prediction algorithms and resolution of the array data. Moreover, better methods are required for interpreting the functional effect of newly discovered CNVs. In this review, we explore current and future application of SNP array technology to assess rare and common CNVs in association with breast cancer risk in humans.
Project description:Cumulative evidence has shown that structural variations, due to insertions, deletions, and inversions of DNA, may contribute considerably to the development of complex human diseases, such as breast cancer. High-throughput genotyping technologies, such as Affymetrix high density single-nucleotide polymorphism (SNP) arrays, have produced large amounts of genetic data for genome-wide SNP genotype calling and copy number estimation. Meanwhile, there is a great need for accurate and efficient statistical methods to detect copy number variants. In this article, we introduce a hidden-Markov-model (HMM)-based method, referred to as the PICR-CNV, for copy number inference. The proposed method first estimates copy number abundance for each single SNP on a single array based on the raw fluorescence values, and then standardizes the estimated copy number abundance to achieve equal footing among multiple arrays. This method requires no between-array normalization, and thus, maintains data integrity and independence of samples among individual subjects. In addition to our efforts to apply new statistical technology to raw fluorescence values, the HMM has been applied to the standardized copy number abundance in order to reduce experimental noise. Through simulations, we show our refined method is able to infer copy number variants accurately. Application of the proposed method to a breast cancer dataset helps to identify genomic regions significantly associated with the disease.
Project description:Copy number variants (CNVs) have repeatedly been found to cause or predispose to autism spectrum disorders (ASDs). For diagnostic purposes, we screened 194 individuals with ASDs for CNVs using Illumina SNP arrays. In several probands, we also analyzed candidate genes located in inherited deletions to unmask autosomal recessive variants. Three CNVs, a de novo triplication of chromosome 15q11-q12 of paternal origin, a deletion on chromosome 9p24 and a de novo 3q29 deletion, were identified as the cause of the disorder in one individual each. An autosomal recessive cause was considered possible in two patients: a homozygous 1p31.1 deletion encompassing PTGER3 and a deletion of the entire DOCK10 gene associated with a rare hemizygous missense variant. We also identified multiple private or recurrent CNVs, the majority of which were inherited from asymptomatic parents. Although highly penetrant CNVs or variants inherited in an autosomal recessive manner were detected in rare cases, our results mainly support the hypothesis that most CNVs contribute to ASDs in association with other CNVs or point variants located elsewhere in the genome. Identification of these genetic interactions in individuals with ASDs constitutes a formidable challenge.