Title:	Data modeling as main source of discrepancies in single and multiple marker association methods
Authors:	LEDUR, M. C. NAVARRO, N. PÉREZ-ENCISO, M.
Affiliation:	MONICA CORREA LEDUR, CNPSA; NICOLAS NAVARRO, UNIVERSIDAD AUTONOMA DE BARCELONA; MIGUEL PÉREZ-ENCISO, ICREA.
Date Issued:	2009
Citation:	BMC Proceedings, Vol. 3, Suppl. 1, 7 p. 2009. Disponível em: <http://www.biomedcentral.com/1753-6561/3/S1/S9>. Acesso em: 08 fev. 2010
Description:	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.
Thesagro:	Genoma
Notes:	Projeto/Plano de Ação: 01.02.102.10-10
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPSA)