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Marker-based estimation of genetic parameters in genomics Open Access

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Author or creator
Hu, Zhiqiu
Yang, Rong-Cai
Additional contributors
Subject/Keyword
Simulation and Modeling
Phenotypes
Regression Analysis
Genome-Wide Association Studies
Covariance
Genetic Polymorphism
Wheat
Population Genetics
Type of item
Journal Article (Published)
Language
English
Place
Time
Description
Linear mixed model (LMM) analysis has been recently used extensively for estimating additive genetic variances and narrow-sense heritability in many genomic studies. While the LMM analysis is computationally less intensive than the Bayesian algorithms, it remains infeasible for large-scale genomic data sets. In this paper, we advocate the use of a statistical procedure known as symmetric differences squared (SDS) as it may serve as a viable alternative when the LMM methods have difficulty or fail to work with large datasets. The SDS procedure is a general and computationally simple method based only on the least squares regression analysis. We carry out computer simulations and empirical analyses to compare the SDS procedure with two commonly used LMM-based procedures. Our results show that the SDS method is not as good as the LMM methods for small data sets, but it becomes progressively better and can match well with the precision of estimation by the LMM methods for data sets with large sample sizes. Its major advantage is that with larger and larger samples, it continues to work with the increasing precision of estimation while the commonly used LMM methods are no longer able to work under our current typical computing capacity. Thus, these results suggest that the SDS method can serve as a viable alternative particularly when analyzing ‘big’ genomic data sets.
Date created
2014
DOI
doi:10.7939/R3BN9XG87
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Attribution 4.0 International
Citation for previous publication
Hu, Z., & Yang, R. -C. (2014). Marker-based estimation of genetic parameters in genomics. PLoS ONE, 9(7), e102715 [12 pages].  http://dx.doi.org/10.1371/journal.pone.0102715

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