This article is part of the supplement: Proceedings of the 12th European workshop on QTL mapping and marker assisted selection
Proceedings
Data modeling as a main source of discrepancies in single and multiple marker association methods
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* Corresponding author: Mônica C Ledur mledur@cnpsa.embrapa.br
- † Equal contributors
1 Embrapa Suínos e Aves, BR 153, Km 110, 89700-000, Concórdia, SC, Brazil
2 Dept. Ciencia Animal i dels Aliments, Facultat de Veterinaria, Universitat Autonoma de Barcelona, 08193, Bellaterra, Spain
3 Institut Català de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain
BMC Proceedings 2009, 3(Suppl 1):S9 doi:
Published: 23 February 2009Abstract
Background
Genome-wide association studies have successfully identified several loci underlying complex diseases in humans. The development of high density SNP maps in domestic animal species should allow the detection of QTLs for economically important traits through association studies with much higher accuracy than traditional linkage analysis. Here we report the association analysis of the dataset simulated for the XII QTL-MAS meeting (Uppsala). We used two strategies, single marker association and haplotype-based association (Blossoc) that were applied to i) the raw data, and ii) the data corrected for infinitesimal, sex and generation effects.
Results
Both methods performed similarly in detecting the most strongly associated SNPs, about ten loci in total. The most significant ones were located in chromosomes 1, 4 and 5. Overall, the largest differences were found between corrected and raw data, rather than between single and multiple marker analysis. The use of raw data increased greatly the number of significant loci, but possibly also the rate of false positives. Bootstrap model aggregation removed most of discrepancies between adjusted and raw data when SMA was employed.
Conclusion
Model choice should be carefully considered in genome-wide association studies.