Comparison of Mixed-Model Approaches for Association Mapping

Stich, Benjamin; Möhring, Jens; Piepho, Hans‐Peter; Heckenberger, Martin; Buckler, Edward S.; Melchinger, Albrecht E.

doi:10.1534/genetics.107.079707

Cited by 229 publications

(234 citation statements)

References 34 publications

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“…In addition, the third criterion for selection of lines included a pre-evaluation for vernalization requirement, thus excluding winter types from the panel. In contrast to these results, Stich et al (2008) implemented GWAS in wheat and found that including P in the mixed model improved control of false positives relative to just including K. However, as pointed out in Stich et al (2008), inclusion of P in the mixed model had a large effect most likely because of the high levels of population structure that exists in wheat.…”

Section: Discussionmentioning

confidence: 90%

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

et al. 2012

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Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F2, backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting β-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2 years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with β-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence homology to genes localized on rice chromosome seven adjacent to the CslFgene family, known to have β-glucan synthase function. Results indicate that GWAS in oat can be a successful option for QTL detection, more so with future development of higher-density markers. Abstract Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F 2 , backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting b-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2 years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with b-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence hom...

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Section: Discussionmentioning

confidence: 90%

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

et al. 2012

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“…Several statistical approaches for JLAM have been suggested based on linear (Yu et al, 2008;Liu et al, 2011) or linear mixed models (Stich et al, 2008). In addition, the QIPDT approach has been suggested (Stich et al, 2006), but the relationship between parents can better be exploited by using linear mixed models including a kinship matrix.…”

Section: Introductionmentioning

confidence: 99%

Comparison of biometrical models for joint linkage association mapping

et al. 2011

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Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C.

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“…Plant materials, phenotypic data and molecular markers To assess the performance of our method, we used three empirical data sets that were described in detail by Stich et al (2008) (winter wheat) and by Stich and Melchinger (2009) (sugar beet and rapeseed).…”

Section: Methodsmentioning

confidence: 99%

“…In the study of Stich et al (2008) the empirical type I error rates of association mapping approaches, which were based on adjusted entry means calculated by a two-step analysis, were only slightly higher than that of approaches in which the phenotypic data analysis and the association analysis were performed in one step (one-step analysis) (also see Mö hring and Piepho, 2009). We therefore calculated adjusted entry means (winter wheat and rapeseed) or entry means (sugar beet) in the first step (for more details, see Stich et al, 2008;Stich and Melchinger, 2009) for each entry under consideration.…”

Section: Statistical Analyses Phenotypic Data Analysesmentioning

confidence: 99%