MONDAY, MARCH 16
7:30 am Registration and Morning Coffee
8:30 Chairperson’s Remarks
Charles Lee, Ph.D., Associate Professor, Patholoy and Cytogenetics, Brigham and Women's Hospital
KEYNOTE PRESENTATION
8:40 Copy Number Variations Associated with Neuropsychiatric Conditions
Stephen W. Scherer, Ph.D., FRSC, Director, The Centre for Applied Genomics Hospital for Sick Children and University of Toronto
Neuropsychiatric conditions such as autism and schizophrenia have long been attributed to genetic alterations, but identifying the genes responsible has proved challenging. Microarray experiments have now revealed abundant variation in the human population of the type known as copy number variation (CNV), in which stretches of DNA are duplicated, deleted, and CNVs can often be rearranged. Genes affected by copy number variation are good candidates for research into disease susceptibility. The complexity of neuropsychiatric genetics, however, dictates that assessing the biomedical relevance of copy number variants and the genes they affect be considered in an integrated context.
DETECTION – TECHNOLOGIES TO DETERMINE CNVs
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9:30 New Target Discovery Via Analysis of Copy Number Variations in Inbred Mice
Paul Kassner, Ph.D., Principal Scientist, Lead Discovery, Amgen
Copy Number Variations (CNVs), though less frequent than SNPs, are an important form of genetic diversity. We have performed the most comprehensive survey to date of CNVs in mice, analyzing the genomes of 42 inbred strains by comparative genomic hybridization (CGH). An average of 11Mb of DNA is found within an average of 56 CNVs across the genomes of each inbred strain. Overall, this results in gene content that can differ by over a hundred genes between the most divergent strains. Because one third of CNVs contain just a single gene, and more than three quarters of CNVs contain at least one gene, we expect that these CNVs result in measurable phenotypic differences. In addition to aiding our understanding of evolution at the genomic level, we demonstrate that the analysis of CNVs can help us understand the mouse models we use for our daily research, as well as lead to the identification of disease-linked genes.
10:00 Mechanisms for Human Genomic Rearrangements
Wenli Gu, Ph.D., Postdoctoral Associate, James Lupski Lab, Department of Molecular & Human Genetics, Baylor College of Medicine
Genomic rearrangements describe structural changes in our genome such as duplication, deletion, insertion, inversion, and translocation that are different from the traditional Watson-Crick base pair alterations. Three major mechanisms—NAHR, NHEJ and FoSTeS have been proposed for genomic rearrangements in the human genome. New experimental data further supporting the DNA replication FoSTeS mechanism will be presented.
10:30 Coffee Break
11:00 Microdissection Molecular Copy Number Counting
Paul Dear, Ph.D., MCR Laboratory for Molecular Biology
A number of tools are widely used for the analysis of CNVs, with micro-arrays being by far the predominant technology. Although arrays have the advantage of analyzing very large numbers of loci simultaneously, they have their limitations. In particular, small or badly-degraded samples (as often encountered in clinical settings) are problematic, as is the analysis of small numbers of loci across large numbers of samples. µMCC complements array platforms; it is not suited to the analysis of very large numbers of loci, but is ideal for assessing CNVs in large numbers of samples, and is capable of giving excellent results from a handful of cells, even when their DNA is badly degraded, as is often the case with clinical specimens. Furthermore, µMCC can also be adapted to give not only copy number information, but structural information on rearranged genomes.
11:30 Panel Discussion with Morning Speakers
12:00 Close of Morning Session
12:15 Luncheon Presentation Sponsored by 
Accurate and Easy Methods to Genotype Copy Number Variants using Nanofluidics and PCR
Robert C. Jones, Executive Vice President Research and Development, Fluidigm Corporation
As CNV-oriented microarray tools have come on-line, discovery of new copy number variants has accelerated, and genome-wide association studies to understand human disease in the context of genomic structural variation have been enabled. These discoveries have created the need for more accurate tools for validation studies of complex genotypes and the need for efficient methods to genotype small numbers of CNV loci in case-cohort studies. The principles of Fluidigm’s nanofluidic dynamic arrays and digital arrays will be introduced with examples of high copy number CNV loci and complex allelic variants. We demonstrate:
• Accurate discrimination of high copy number variants.
• Complex genotyping of SNP and CNV assayed together in single end-point assays.
• Cost effectiveness for case-cohorts studies where the number of loci is small but the number of samples is large.
• Increased assay robustness because digital assays are less affected by PCR efficiency, pipetting variation and polymorphism in the PCR priming sites.
DATA – INTEGRATING CNVs WITH ASSOCIATION STUDIES
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2:00 Chairperson’s Remarks
Jan Korbel, Ph.D., Group Leader, Gene Expression Unit, European Molecular Biology Laboratory
2:05 Integrating the Analysis of Copy Number and Gene Expression Data
Mike Lelivelt, Ph.D., Vice President, Genomics, Partek, Inc.
The central dogma of molecular biology states that information flows from nucleic acids to proteins. However, most modern genomics or transcriptomic analysis methods still treat analysis of genome level nucleic acids as a collection of independent analysis silos. This talk will present an overview of several methods to combine data between copy number and gene expression analyses. Special emphasis will be given on how the problem of applying sometimes arbitrary filters to data sets can be mitigated using a multiple-assay approach.
2:35 Accurate and Objective Copy Number Profiling using Real-Time PCR
Jan Hellemans, Ph.D., Center for Medical Genetics, Ghent University Hospital; Co-Founder, Biogazelle
Real-time quantitative PCR (qPCR) is the gold standard for the accurate quantification of nucleic acids. While it is widely used for gene expression analysis, the method is also perfectly suited for detection of copy number variants. We have previously shown that multiple technical replicates, reference targets and inter-run calibrators improve the accuracy of the results. Here, we extend this series of ‘more is better’ to include multiple reference samples (with known copy number) for more accurate copy number quantification. The inclusion of both a normal and a deletion control as a reference resulted in less false positive copy number variants. We further outline a data analysis strategy based on z-score distribution of copy numbers in normal controls for statistical assessment of copy number variation in patients. These methods have been applied for accurate and objective copy number profiling in a large scale study on two sets of 100 patients for a total of 70 amplicons.
3:05 Great Diversities in Genotypes and Phenotypes Associated with High Frequency CNVs in Human Populations
C. Yung Yu, Ph.D., Professor, Pediatrics; Molecular Virology, Immunology and Medical Genetics, The Ohio State University, and Center for Molecular and Human Genetics, The Research Institute at Nationwide Children’s Hospital
Described in this talk will be segmental duplications with genes coding for complement C4 and steroid 21-hydroxylase in RCCX modules among different ethnic groups; C4 gene copy number variations in autoimmune disease systemic lupus erythematosus; and recombinations between functional genes and pseudogenes in length variants of RCCX modules contributing to genetic disease congenital adrenal hyperplasia.
3:35 Analysis of Genome-Wide Genotyping Data for Copy Number Variation in Breast Cancer
Sponsored by 
Aubree Hoover, Senior Product Manager, Genomics, Rosetta Biosoftware
Altered copy number of genes and regulatory regions of the genome can affect human health. Analysis of copy number variation (CNV) is of interest in the context of cancer research. High-density single nucleotide polymorphism (SNP) arrays generate intensity based data which can be analyzed for CNV. Using intensity data from Affymetrix(r) SNP arrays, CNV was assessed for breast cancer samples and data was imported into the Rosetta Syllego system and analyzed for CNV. Results were stored in the Syllego database and evaluated with built-in data viewers. This talk will discuss the results of this analysis and how the Syllego system simplifies transfer, visualization, interpretation, and sharing of data with colleagues.
3:50 Refreshment Break
4:15 Detecting CNVs with DNA Sequence-Based Analyses
Jan Korbel, Ph.D., Group Leader, Gene Expression Unit, European Molecular Biology Laboratory
Our laboratory is interested in the extent, origin, and functional consequences of genetic variation, in particular structural variants such as CNVs, which we study by combining bioinformatics and genomics. Presented will be a recently developed high-resolution and massive paired-end mapping approach to obtain genomic records of structural variants at near kilobase-level resolution and systematically analyze their breakpoints in two individuals. Also presented are new approaches for detecting structural variants from sequence-based analyses, with a particular focus on the mining of data generated with novel large-scale DNA sequencing technologies. We used extensive simulations for parameterization of the approaches and for benchmarking, enabling us to detect structural variants from large sequence datasets at high specificity and sensitivity, and within a reasonable time-scale.
4:45 Structural Variation and Human Disease
Evan Eichler, Ph.D., Professor, Genome Sciences, University of Washington
Structural variation of the genome is an important aspect in our understanding of human disease and susceptibility to disease. I will focus on the genome-wide discovery, analysis and distribution of copy-number and structural variants within the “normal” human population with a particular emphasis on resolving these events at the single base pair level. Using this data, I will show how high-quality sequence data can be used to predict regions associated with disease and the discovery of the molecular basis for new syndromes and recurrent events associated with more complex diseases. I will present data showing how common structural polymorphisms may predispose to genetic disease and how historical hotspots of variation can be used to identify previously undescribed microdeletion and microduplication syndromes associated with various forms of pediatric disease including mental retardation, epilepsy, diabetes and renal disease.
5:15 A Single-Array Preprocessing Method for Estimating Full-Resolution Raw Copy Numbers from all Affymetrix Genotyping Arrays
Henrik Bengtsson, Ph.D., Post Doctoral Fellow, Department of Statistics, University of California, Berkeley
We propose a single-array preprocessing method for estimating full-resolution total copy numbers (CNs). It is applicable to all Affymetrix genotyping arrays including the ones with non-polymorphic probes. As with our CRMA method for earlier generations of arrays, this one controls for allelic crosstalk, probe affinities and PCR fragment-length effects. Additionally, it also corrects for probe-sequence effects and co-hybridization of fragments digested by multiple enzymes that takes place on the latest chips. Using HapMap data, we compare our method with Affymetrix’ CN5 method and the dChip method, by assessing how well they differentiate between various CN states at the full resolution and at various amounts of smoothing. CRMA v2 outperforms the other multi-array methods, which shows that it is possible to do single-array preprocessing of CN data. This has several useful implications on how data can be analyzed: (i) only two hybridizations are needed for a tumor/normal comparison, (ii) in medial diagnostic individual patients can be analyzed at once even when they come singly rather than in batches, (iii) large-scale data sets can be processed faster by utilizing parallel computations, and so on.
5:45 Grand Opening of the Exhibit Hall
7:00 Close of Day