Empirical Comparisons of Different Statistical Models To Identify and Validate Kernel Row Number-Associated Variants from Structured Multi-parent Mapping Populations of Maize

Authors

Jinliang Yang, University of Nebraska-LincolnFollow
Cheng-Ting Yeh, Iowa State University
Raghuprakash Kastoori Ramamurthy, University of Nebraska-LincolnFollow
Xinshuai Qi, University of Arizona
Rohan L. Fernando, Iowa State University
Jack C. M. Dekkers, Iowa State University
Dorian J. Garrick, Iowa State UniversityFollow
Dan Nettleton, Iowa State University
Patrick Schnable, Iowa State UniversityFollow

ORCID IDs

0000-0002-0999-3518

0000-0002-1392-2018

0000-0002-7804-712X

0000-0001-5821-099X

0000-0001-9169-5204

Document Type

Article

Date of this Version

2018

Citation

Genes, Genomics, Genetics Volume 8

Comments

Copyright © 2018 Yang et al.

Open access

DOI: 10.6084/m9.figshare.6902144

Abstract

Advances in next generation sequencing technologies and statistical approaches enable genomewide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development.