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UW Researchers Develop New Array for Copy Number Polymorphism Genotyping

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By Justin Petrone

Researchers at the University of Washington in Seattle have developed a new microarray that they claim can be used to look at copy number polymorphisms in human populations.

Copy number polymorphisms, or CNPs, are copy number variants that are present at a high frequency in humans — generally more than 1 percent of the population. CNPs are enriched in segmentally duplicated regions of the genome, often for immune and environmental response functions.

In addition, several CNPs have been associated with human diseases, such as lupus, psoriasis, Crohn's disease, and obesity, the researchers noted in a paper describing the new chip in the American Journal of Human Genetics.

Working with partners at Agilent Technologies, the research team showed that CNPs in segmental duplications tend to be differentiated across different human populations, suggesting that these variants are an "important contributor to genetic diversity," according to lead author Catarina Campbell.

Campbell told BioArray News last week that the researchers designed an array of CNPs for the project based on prior work. "We selected the CNPs for this study from a number of previously published CNP screens," she said. The largest numbers of targeted CNPs were discovered using a clone end mapping approach, Campbell noted. In addition, the researchers targeted CNPs found using SNP microarray platforms.

Specifically, the researchers designed a custom 180,000-probe microarray on the Agilent 4X180K SurePrint G3 Human CGH Microarray Platform. They targeted 4,041 known CNPs. Since CNPs are enriched in regions of segmental duplication, the loci targeted on the group's microarray are enriched for segmental duplication content compared to the genome as a whole, according to the paper.

The new array also targeted 1,269 insertions that are not in the reference genome assembly and included 6,899 probes designed to target copy-number invariable regions of the genome as well as 3,000 standard Agilent normalization probes.

The authors believe the new platform offers advantages over existing tools, such as SNP genotyping microarrays and next-generation sequencing.

"Although copy number can be accurately assessed with next-generation sequencing, these methodologies depend on whole-genome sequencing data, which is expensive to obtain on a large number of individuals for disease association," the authors write in the paper. Existing SNP arrays "lack probes in many known CNP loci, especially variants in segmental duplications," they also note.

"Although our custom microarray also does not test the comprehensive landscape of CNPs, we believe that this microarray complements other microarrays by targeting CNPs that are not well captured on other platforms," they concluded.

To validate the array, Campbell and fellow researchers conducted a screen of individuals from five different populations. They also developed a method based on single-channel intensity data and benchmarked against copy numbers determined from sequencing read depth to obtain CNP genotypes for 1,495 CNPs from 487 human DNA samples.

The researchers also matched their results against existing array platforms. They found that 937 of the 1,495 polymorphic loci that perform well on the CNP microarray were covered by less than five probes on both the Affymetrix 6.0 and the Illumina 1M SNP microarrays. In addition, 808 of the 1,495 loci have not been tested in a large CNP association study.

Campbell said that the CNP array design and raw data from the validation study are now available in the National Center for Biotechnology Information's Gene Expression Omnibus database with the accession number GSE2645.

Going forward, she said that the researchers intend to further develop the array. "We do plan to expand upon the design as new CNPs are discovered, and large population-based sequencing studies, like the 1000 Genome Project, will increase our knowledge of the CNP landscape," she said.


Have topics you'd like to see covered in BioArray News? Contact the editor at jpetrone [at] genomeweb [.] com.