Citing "rapid adoption" of arrays with copy number variation content for use in genome-wide association studies, Roche NimbleGen plans to launch a menu of new CNV chips.
The firm intends to debut a 2.1-million feature array and a chip with three 720,000-feature arrays containing CNVs that could be implicated in a "range of common and complex diseases," including autism, schizophrenia, autoimmune diseases, and HIV risk, according to spokesperson Kary Staples.
"With the emergence of high-resolution maps of CNVs, their impact on disease phenotypes has become a key research focus," Staples says. "Life science researchers are rapidly adopting the use of CNV arrays for large-scale genome-wide association studies."
According to Vanessa Ott, Roche NimbleGen's CNV array product manager, the new chips will contain targeted content culled from several sources. These include the Centre for Applied Genomics-hosted Database of Genomic Variants and the Genome Structural Variation Consortium's CNV discovery project, which used Roche's 2.1 million-feature arrays to create a high-resolution map of human CNVs based on the analysis of 20 European and 20 African HapMap samples.
The arrays will also include CNV content from Asian samples identified using Roche NimbleGen's CGH 2.1M whole-genome tiling arrays. In July, Roche announced a partnership with the Korean Centers for Disease Control and Prevention and Seoul-based service provider Macrogen to characterize CNVs related to common diseases in the Korean population.
According to Roche NimbleGen, 99 percent of the CNVs on its new arrays will be represented by at least five probes, and 84 percent by at least 10 probes. The arrays provide "backbone coverage of the entire human genome to enable detection of novel CNVs down to approximately 5 kilobase resolution," Roche says.
Roche will include CNVs from different ethnic populations because "recent data suggest that there are CNVs specific to [them],"Ott says.
— Justin Petrone
In work appearing in Nature, an international team led by scientists at the Wellcome Trust Sanger Institute used tiling arrays to discover almost 12,000 common CNV candidates from dozens of HapMap samples. They then used arrays to generate genotype information for roughly 5,000 CNVs.
Researchers from the US, Nigeria, and Senegal combined sequencing data and arrays to create a high-density consensus linkage map for the legume Vigna unguiculata, commonly known as cowpea. The work appeared in PNAS.
A study in Nature Genetics from NIH scientists showed that a certain statistical approach used with genotype frequencies and individual genotype information could identify GWAS participants — or their close relatives — from aggregate GWAS data.
Amount NIH will spend to fund the creation of an online database of copy number variation information related to abnormal phenotypes.
Statistical methods for assessing copy number variation in SNP arrays
Grantee: Robert Scharpf, Johns Hopkins University
Began: Jul. 24, 2009; Ends: Jun. 30, 2010
With this award, Scharpf will improve the science of estimating copy number and inferring regions of CNV by developing first-generation algorithms for problems by locus, by sample, and between samples. His goal is "to establish an interdisciplinary research lab in biostatistics and human genetics that supports creative computational and statistical solutions to ... genomic data."
Extending and Interpreting Molecular Portraits of Cancer
Grantee: Patrick Brown, Stanford University
Began: Aug. 1, 2006; Ends: Jun. 30, 2011
Brown will use DNA and tissue microarrays to profile gene and protein expression patterns in different cancers to discover new molecular markers. He will focus on characterizing the cells in the microenvironment of cancer tissues; three molecular subtypes of prostate cancer; small, early-stage breast cancers; and soft-tissue sarcomas.