A comparison of genomic copy number calls by Partek Genomics Suite, Genotyping Console and Birdsuite algorithms to quantitative PCR

<p>Abstract</p> <p>Background</p> <p>Copy number variants are >1 kb genomic amplifications or deletions that can be identified using array platforms. However, arrays produce substantial background noise that contributes to high false discovery rates of variants. We hypothesized that quantitative PCR could finitely determine copy number and assess the validity of calling algorithms.</p> <p>Results</p> <p>Using data from 29 Affymetrix SNP 6.0 arrays, we determined copy numbers using three programs: Partek Genomics Suite, Affymetrix Genotyping Console 2.0 and Birdsuite. We compared array calls at 25 chromosomal regions to those determined by qPCR and found nearly identical calls in regions of copy number 2. Conversely, agreement differed in regions called variant by at least one method. The highest overall agreement in calls, 91%, was between Birdsuite and quantitative PCR. Partek Genomics Suite calls agreed with quantitative PCR 76% of the time while the agreement of Affymetrix Genotyping Console 2.0 with quantitative PCR was 79%.</p> <p>Conclusions</p> <p>In 38 independent samples, 96% of Birdsuite calls agreed with quantitative PCR. Analysis of three copy number calling programs and quantitative PCR showed Birdsuite to have the greatest agreement with quantitative PCR.</p

NEW SOFTWARE FOR PROCESSING DATA OF ENTIRE GENOME OF MYCOBACTERIUM TUBERCULOSIS

The article describes the software developed for processing of data of entire genomes of human tuberculous mycobacteria

Assessment of Genome-Wide Genetic and Epigenetic De Novo Variation in Families with Monozygotic Twins Discordant for Schizophrenia

Schizophrenia (OMIM: 181500) is a common, debilitating and life-altering disorder. It affects 1% of the population worldwide and most often presents in early adulthood leading to devastating effects for patients, their families and society. Despite thousands of studies performed on the underlying mechanisms of schizophrenia, the causes of the disease remain unknown. However, what is known is that environmental, genetic and epigenetic factors contribute to the development of this complex disorder. Although a genetic role in schizophrenia is well established, the search for schizophrenia genes using traditional approaches has remained challenging. Interestingly, monozygotic twins show concordance for schizophrenia only 50% of the time and therefore provide a unique scenario for genomic analysis. This Doctoral thesis examines the genetic and epigenetic contributions to schizophrenia discordance in monozygotic twins. In this thesis, I have identified and characterized genome-wide changes through the use of the Affymetrix SNP 6.0 Microarray, Complete Genomics whole genome sequencing and the Nimblegen Methylation 720k Microarray. Specifically, I have identified genetic and epigenetic differences between monozygotic twins discordant for schizophrenia. The results show multiple genetic and epigenetic changes between monozygotic twins with discordance for schizophrenia. Some of these differences are patient-specific and others are shared between affected twins in the study. In addition, some of these differences affected genes and others did not. Many of the genes and genomic regions have been previously implicated in schizophrenia and neurodevelopmental disorders. The findings reinforce the concept that individual genomes harbor extensive variability, some inherited and some acquired. Even monozygotic twins are not identical and each individual may be a mosaic; carrying different sequence variations in different cells. The results also suggest that discordance for schizophrenia in monozygotic twins may result from the accumulation of genetic and epigenetic mutations that lead to the disease threshold being met in one twin only. The results argue for the involvement of de novo mutations in genetic individuality and complex disease. Improved understanding of the genomic contributions to schizophrenia is critical for movement towards earlier and more accurate diagnosis, better treatment and further understanding of this complex mental health disorder

Database Methods for Copy Number Variant Analysis of One Hundred Disease Associated Genes in Human Congenital Heart Disease

Human genetic variation occurs more commonly than was recognized after the completion of the Human Genome Sequencing Project in 2003. Submicroscopic human DNA analysis has revealed copy number variation (CNV) as the deletion or duplication of a genomic region potentially affecting gene dosage. Advanced genetic research now includes the study of CNVs in diseased subject groups compared to in house controls or online published datasets of control CNV data. Research labs choose from different bioinformatic algorithms to make the copy number calls. Solutions for further processing the copy number data into quantifiable form require collaboration with data analysts and include the use of relational databases. The aim of this thesis work was to develop a relational database solution for human copy number variation in subjects with cardiac malformations. The multipurpose database served as a central repository for the cohort demographic data as well as the entire experimental set of copy number variant data. Quantification and frequency analyses of the CNVs were executed via SQL queries. Database SQL queries generated raw data used for essential visualization tools including a detailed subject profile and a one hundred gene CNV spectra. The stated purpose of the study was to develop a descriptive analysis of genomic copy number associations in a well phenotyped congenital heart disease (CHD) population over one hundred disease associated genes. The relational database created to advance the research proved valuable in its data storage and retrieval capacity. Results showing consistency with published literature validated the accuracy of the query results generated for the CHD cohort

Somatic Copy Number Mosaicism Contributes to Genomic Diversity in Mus musculus

Copy number variants (CNVs) are a source of genomic variation associated with altered phenotypes. Somatic copy number mosaicism results when different populations of cells in an individual differ due to de novo copy number changes (CNCs). Tissue-specific patterns of CNCs resulting in mosaicism have yet to be characterized in the mouse, an organism frequently used to model human diseases. Here, DNA was sampled from spleen, liver, and cerebellum of eight highly related mice selected from a familial unit. CNVs and CNCs were detected using the Mouse Diversity Genotyping Array with three computational methods (ConsecN, Partek, and PennCNV). Tissue-specific patterns of CNCs were revealed, including genomic regions of putative recurring CNCs. Genetic distance estimated using CNVs and CNCs recapitulated genealogical relationships. The novel framework can thus be used to identify and analyze tissue-specific CNCs, and the results establish the need to account for CNCs in model organisms

Towards a comprehensive structural variation map of an individual human genome

A comprehensive map of structural variation in the human genome provides a reference dataset for analyses of future personal genomes