Campus Units

Agronomy, Animal Science, Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

11-2018

Journal or Book Title

G3: Genes, Genomes, Genetics

Volume

8

Issue

11

First Page

3567

Last Page

3575

DOI

10.1534/g3.118.200636

Abstract

Advances in next generation sequencing technologies and statistical approaches enable genome-wide 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.

Comments

This article is publsihed as Yang, Jinliang, Raghuprakash Kastoori Ramamurthy, Xinshuai Qi, Rohan L. Fernando, Jack CM Dekkers, Dorian J. Garrick, Dan Nettleton, and Patrick S. Schnable. "Empirical Comparisons of Different Statistical Models To Identify and Validate Kernel Row Number-Associated Variants from Structured Multi-parent Mapping Populations of Maize." G3: Genes, Genomes, Genetics 8, no. 11 (2018): 3567-3575. doi: 10.1534/g3.118.200636.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Yang et al.

Language

en

File Format

application/pdf

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