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Abstract #166833

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Benilton S. Carvalho and Rafael Irizarry, PhD. Department of Biostatistics, Johns Hopkins University, Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, 410-955-3067, bcarvalh@jhsph.edu

The discovery of markers associated to a given trait makes constant use of oligonucleotide microarrays. The initial phases of an association study is often linked to genomewide scan and the SNP chips show their relevance at this step, when thousands of SNP's can be genotyped at once. Earlier versions had a coverage of 10 thousands SNP's, today this number is closer to 1 million SNP's.

Academic groups have demonstrated that the methods offered by the manufacturers for preprocessing microarray data can be significantly improved, allowing the researcher to make inferences more precisely.

We propose new preprocessing and genotyping algorithms, SNPRMA and CRLMM, which outperform the standard approach, PLIER and BRLMM, in a number of assessments.

We illustrate our strategy with datasets comprised of arrays of different qualities and demonstrate our ability to predict accuracy for each genotype call, feature not directly available from other methodologies.

Learning Objectives:

• • Introduce the SNP chip technology;
• • Demonstrate the importance for preprocessing;
• • Discuss the CRLMM algorithm;
• • Discuss assessments’ results.

Keywords: Genetics, Biostatistics

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

• Audio (mp3) recording