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Next generation modeling in GWAS: comparing different genetic architectures

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Abstract

The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.

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Acknowledgments

This study was partially conducted in the Biosciences Research Division, Department of Environment and Primary Industries (Melbourne, Australia). Many thanks to Phil Bowman for his help using the clusters. We also acknowledge the principal investigators, coordinators, field and administrative workers, technicians and study participants of the Spanish Bladder Cancer/EPICURO study.

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Authors and Affiliations

  1. Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/MelchorFernándezAlmagro, 3, 28029, Madrid, Spain

    Evangelina López de Maturana, Gaëlle Marenne & Núria Malats

  2. Genética i Millora Animal, Research Institute and Agricultural Technology (IRTA), Avd. Alcalde Rovira Roure 191, 25198, Lleida, Spain

    Noelia Ibáñez-Escriche & Hossein Mehrban

  3. Biosciences Research Division, Department of Environment and Primary Industries, Agribio, 5 Ring Road, Bundoora, VIC, 3083, Australia

    Óscar González-Recio & Michael E. Goddard

  4. Dairy Futures Cooperative Research Centre, Bundoora, VIC, 3083, Australia

    Óscar González-Recio

  5. Department of Animal Science, University of Shahrekord, P.O. Box 115, 88186-34141, Shahrekord, Iran

    Hossein Mehrban

  6. Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, Bethesda, MD, USA

    Stephen J. Chanock

  7. Department of Food and Agricultural Systems, University of Melbourne, Melbourne, Australia

    Michael E. Goddard

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  1. Evangelina López de Maturana
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  2. Noelia Ibáñez-Escriche
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  3. Óscar González-Recio
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  8. Núria Malats
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Correspondence to Evangelina López de Maturana.

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López de Maturana, E., Ibáñez-Escriche, N., González-Recio, Ó. et al. Next generation modeling in GWAS: comparing different genetic architectures. Hum Genet 133, 1235–1253 (2014). https://doi.org/10.1007/s00439-014-1461-1

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