An Arabidopsis Example of Association Mapping in Structured Samples

By Keyan Zhao, M. Aranzana, Sung K. Kim, C. Lister, C. Shindo, Chunlao Tang, C. Toomajian, Honggang Zheng, C. Dean, Paul Marjoram and 1 more. PLoS Genetics, 2006

755 citations recorded by the source corpus.

A potentially serious disadvantage of association mapping is the fact that marker-trait associations may arise from confounding population structure as well as from linkage to causative polymorphisms. Using genome-wide marker data, we have previously demonstrated that the problem can be severe in a global sample of 95 Arabidopsis thaliana accessions, and that established methods for controlling for population structure are generally insufficient. Here, we use the same sample together with a number of flowering-related phenotypes and data-perturbation simulations to evaluate a wider range of methods for controlling for population structure. We find that, in terms of reducing the false-positive rate while maintaining statistical power, a recently introduced mixed-model approach that takes genome-wide differences in relatedness into account via estimated pairwise kinship coefficients generally performs best. By combining the association results with results from linkage mapping in F2 crosses, we identify one previously known true positive and several promising new associations, but also demonstrate the existence of both false positives and false negatives. Our results illustrate the potential of genome-wide association scans as a tool for dissecting the genetics of natural variation, while at the same time highlighting the pitfalls. The importance of study design is clear; our study is severely under-powered both in terms of sample size and marker density. Our results also provide a striking demonstration of confounding by population structure. While statistical methods can be used to ameliorate this problem, they cannot always be effective and are certainly not a substitute for independent evidence, such as that obtained via crosses or transgenic experiments. Ultimately, association mapping is a powerful tool for identifying a list of candidates that is short enough to permit further genetic study.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/1275b5d0ab60cdccd79cd36146ec755f6cbbd664

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Citations in the corpus

Status and Prospects of Association Mapping in Plants (2008, methodology): “E stands for residual variance. structure can now be dealt with accordingly (Price et al., 2006; Yu et al., 2006; Zhao et al., 2007).”
Association Mapping in Plant Genomes (2012, background): “Canola Diverse accessions Leaf traits, flowering time and phytate content AFLPs Zhao et al. (2007b)”
Association Mapping: Critical Considerations Shift from Genotyping to Experimental Design (2009, methodology): “Estimation of the Q matrix using STRUCTURE is computationally intensive and is designed for unrelated individuals from populations in Hardy-Weinberg equilibrium, whereas PCA is fast, makes no assumptions about the structure of the populations, and performs similarly or better than STRUCTURE (Zhao et”
Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies (2016, methodology): “In general, theQ+K model performs better than theQmodel or the Kmodel alone when they can not be inclusively represented each other[9,14].”
Cell Membrane Stability and Association Mapping for Drought and Heat Tolerance in a Worldwide Wheat Collection (2017, methodology): “A linear mixed model was used in which K and Q + K [68] matrixes were fitted.”
Population Structure and Linkage Disequilibrium in U.S. Barley Germplasm: Implications for Association Mapping (2010, background): “Methodologies accounting for population structure in GWAS have been the subject of many papers in human and plant genetics (e.g., Marchini et al., 2004; Yu et al., 2006; Zhao et al., 2007), but the population genetic properties of barley are very diff erent from those of natural populations such as ”
A Multiparent Advanced Generation Inter-Cross to Fine-Map Quantitative Traits in Arabidopsis thaliana (2009, background): “The MAGIC lines represent a compromise between the extreme simplicity of a diallelic system found in a RIL panel descended from just two progenitors with no population structure other than that due to segregation distortion [48], and the much greater complexity encountered in the natural accessions ”
Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program (2011, methodology): “…random covariate, a simple identity-by-state allele sharing kinship matrix (K), to account for genetic covariance among individuals to reduce the number of false positive marker–trait associations caused by population structure and genetic relatedness (Zhao et al., 2007; Kang et al., 2008).”
FaST linear mixed models for genome-wide association studies (2011, methodology): “The problem of confounding by population structure, family structure, and cryptic relatedness in genome-wide association studies (GWAS) is widely appreciated [1, 2, 3, 4, 5, 6, 7].”
Variance component model to account for sample structure in genome-wide association studies (2010, positive): “Similar variance component approaches have been used successfully in animal model”
The advantages and limitations of trait analysis with GWAS: a review (2013, background): “applied to GWAS [11,51-53], and can markedly reduce the number of false positive associations (Figure 3).”
Genome-wide association study of 107 phenotypes in a common set of Arabidopsis thaliana inbred lines (2010, neutral): “A major difference between our study and the human GWA studies published to date is that our study population is heavily structured, and there is thus every reason to expect elevated false-positive rates 9 .”
Overview and Application of QTL for Adult Plant Resistance to Leaf Rust and Powdery Mildew in Wheat (2014, background): “Single nucleotide polymorphisms are ideally suited for construction of high-resolution genetic maps, investigations of population evolutionary history, and discovery of marker–trait associations (Aranzana et al., 2005; Zhao et al., 2007).”