Quick Search:

uzh logo
Browse by:

Zurich Open Repository and Archive

Maintenance: Tuesday, July the 26th 2016, 07:00-10:00

ZORA's new graphical user interface will be relaunched (For further infos watch out slideshow ZORA: Neues Look & Feel). There will be short interrupts on ZORA Service between 07:00am and 10:00 am. Please be patient.

Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-29877

Stassen, H H; Hoffmann, K; Schrfetter, C (2009). The Difficulties of Reproducing Conventionally Derived Results through 500k-Chip Technology. BMC Proceedings:S66.



ABSTRACT : Based on a "training" sample of 1,042 subjects genotyped for 5,728 single-nucleotide polymorphisms (SNPs) of a conventional 0.4-Mb genome scan and a "test" sample of 746 subjects genotyped for 545,080 SNPs on a 500k-chip, we investigated the extent to which the subjects' immunoglobulin M levels can be reproducibly predicted from a multilocus genotype. We were specifically interested in the reproducibility of predictors across populations (1,042 versus 746 subjects) and across SNP sets (conventional genome scan versus anonymous 500k-chip) because this is a prerequisite for clinical application. For the training sample, neural network (NN) analysis yielded classifiers that predicted immunoglobulin M levels from the subjects' multilocus genotypes at acceptable error rates through a configuration of 15 genomic loci (61 SNPs). With the test sample (746 subjects) we addressed the question of reproducibility across populations and across SNP sets by means of a novel "competitive SNP set" approach. However, the chip data contained several sources of distortion, including greatly elevated noise levels and artifact-prone SNP regions, thus complicating attempts to verify the reproducibility of NN predictors. Though 5 of 15 genomic loci from the training samples appeared to be reproducible, the NN classifiers derived so far from the test samples are insufficiently compatible with the training samples. Nonetheless, our results are promising enough to justify further investigations. Because the underlying algorithm can easily be split into parallel tasks, the proposed "competitive SNP set" approach has turned out to be well suited for computers with today's 64-bit multiprocessor architectures and to offer a valuable extension to genome-wide association analyses.



29 downloads since deposited on 15 Feb 2010
14 downloads since 12 months

Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
Dewey Decimal Classification:610 Medicine & health
Deposited On:15 Feb 2010 09:11
Last Modified:05 Apr 2016 13:53
Publisher:BioMed Central
Free access at:PubMed ID. An embargo period may apply.
Related URLs:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795967/?tool=pubmed (Publisher)
PubMed ID:20018060

Users (please log in): suggest update or correction for this item

Repository Staff Only: item control page