@article{0e163a11f61c499ab78770fef758295f,
title = "An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and number of markers",
abstract = "Association studies have been successful at identifying genomic regions associated with important traits, but routinely employ models that only consider the additive contribution of an individual marker. Because quantitative trait variability typically arises from multiple additive and non-additive sources, utilization of statistical approaches that include main and two-way interaction marker effects of several loci in one model could lead to unprecedented characterization of these sources. Here we examine the ability of one such approach, called the Stepwise Procedure for constructing an Additive and Epistatic Multi-Locus model (SPAEML), to detect additive and epistatic signals simulated using maize and human marker data. Our results revealed that SPAEML was capable of detecting quantitative trait nucleotides (QTNs) at sample sizes as low as n = 300 and consistently specifying signals as additive and epistatic for larger sizes. Sample size and minor allele frequency had a major influence on SPAEML{\textquoteright}s ability to distinguish between additive and epistatic signals, while the number of markers tested did not. We conclude that SPAEML is a useful approach for providing further elucidation of the additive and epistatic sources contributing to trait variability when applied to a small subset of genome-wide markers located within specific genomic regions identified using a priori analyses.",
author = "Chen, {Angela H.} and Weihao Ge and William Metcalf and Eric Jakobsson and Mainzer, {Liudmila Sergeevna} and Lipka, {Alexander E.}",
note = "Funding Information: Acknowledgements We would like to thank Timothy M. Beissinger, Gota Morota, and three anonymous referees for their helpful suggestions and insight towards the contribution of epistatic factors to phenotypic variability. Their collective suggestions have made this work stronger and more relevant to quantitative genetics research endeavors. We also would like to acknowledge Thierry Schuepbach for his assistance with assessing the FastEpistasis results presented in this paper. The Alzheimer{\textquoteright}s Disease study data were provided by the Mayo Clinic Alzheimer{\textquoteright}s Disease Genetic Studies, led by Dr. Nilufer Taner and Dr. Steven G. Younkin, Mayo Clinic, Jacksonville, FL using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer{\textquoteright}s Disease Research Center, and the Mayo Clinic Brain Bank. AD data collection was supported through funding by NIH grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation, and support from Mayo Foundation. The research conducted in this manuscript was supported by the United States Department of Agriculture National Institute of Food and Agriculture project accession number 1005564, the University of Illinois Comp-Gen Student Fellowship Program, the University of Illinois startup funds, and the UIUC Center for Computational Biotechnology and Genomic Medicine (06/01/2017-06/01/2018 NSF IUCRC 2017 at UIUC). We are grateful to the National Center for Supercomputing Applications for the generous provision of computational resources and administrative support. Special thanks to Katherine Kendig for editorial comments. Publisher Copyright: {\textcopyright} 2018, The Author(s).",
year = "2019",
month = may,
day = "1",
doi = "10.1038/s41437-018-0162-2",
language = "English (US)",
volume = "122",
pages = "660--671",
journal = "Heredity",
issn = "0018-067X",
publisher = "Springer Nature",
number = "5",
}