A unified mixed-model method for association mapping that accounts for multiple levels of relatedness

By Jianming Yu, G. Pressoir, W. Briggs, I. V. Bi, M. Yamasaki, John Doebley, Michael D. McMullen, Brandon S. Gaut, D. Nielsen, Jim Holland and 2 more. Nature Genetics, 2006

3,948 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/c850813fe8d6249806ead14f7fcad21e7932f21d

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Citations in the corpus

Association Mapping for Grain Quality in a Diverse Sorghum Collection (2012, methodology): “We followed the unii ed mixed model approach to account for spurious associations that result from population structure and familial relatedness (Yu et al., 2006).”
An efficient multi-locus mixed model approach for genome-wide association studies in structured populations (2012, neutral): “Several methods have been proposed to deal with this issue, including genomic control 3 , structured association 4 , principal components analysis 5 , and mixed linear models 6 .”
Genomic Selection for Crop Improvement (2017, neutral): “Recent developments in the public sector are promising, such as the Barley Coordinated Agricultural Project Hordeum Toolbox (http:// hordeumtoolbox.org/), the GDPDM database schema that links with the association analysis software TASSEL (http://www.maizegenetics.net); the German GABI-BRAIN project ”
Robust Identification of Local Adaptation from Allele Frequencies (2012, background): “…is conceptually similar to linear mixed model (LMM) approaches that account for kinship structure in genome-wide association studies (GWAS) (e.g. Yu et al., 2006; Kang et al., 2008, 2010; Zhou and Stephens, 2012, who use an observed relatedness matrix as the covariance matrix of the random…”
The confounding effect of cryptic relatedness for environmental risks of systolic blood pressure on cohort studies (2013, background): “First, using the techniques to detect and correct for unrecognized population structure in GWAS, we examined how CR was presented in the sampling.”
Variance component model to account for sample structure in genome-wide association studies (2010, neutral): “Similar variance component approaches have been used successfully in animal model”
Association Analysis as a Strategy for Improvement of Quantitative Traits in Plants (2006, background): “More complex models can accommodate different levels of relationship and admixture of subpopulations ( Yu et al., 2006 ).”
GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction (2020, methodology): “468 469 For the conventional method of single-locus test, many advanced methods were developed, 470 including incorporation of population structure [2], kinship [34], compressed kinship [35], and 471 complementary kinship [11,36].”
Molecular marker-assisted breeding options for maize improvement in Asia (2010, background): “…genomic, and statistical tools are now at hand to successfully apply association mapping for the dissection of complex traits in plants, which will harness the natural diversity in the croprelated gene pool to identify and use allelic variants for crop improvement (Yu et al. 2006; Zhu et al. 2008).”
Applications of Linkage Disequilibrium and Association Mapping in Maize (2009, methodology): “Taking this mixed model framework, Yu et al. (2006b) suggested replacing the pedigree-based co-ancestry with a marker-based relative kinship (K) to account for the relatedness among individuals.”
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).”
Association mapping of quality traits in potato (Solanum tuberosum L.) (2008, background): “With a limited number of markers and relatively simple regression models we detected marker-trait associations for a number of quality traits such as cold sweetening (Li et al. 2005; Menendez et al. 2002), after cooking darkening (ACD) (Friedman 1997; Wang-Pruski and Nowak 2004 ), enzymatic brownin”
The genetic dissection of quantitative traits in crops (2010, methodology): “For association mapping, the populations can be classified into one of the following five groups (Yu and Buckler, 2006; Yu et al. 2006): (i) ideal sample with subtle population structure and familial relatedness, (ii) multi-family sample, (iii) sample with population structure, (iv) sample with…”
Molecular Markers and Selection for Complex Traits in Plants: Learning from the Last 20 Years (2008, methodology): “Association mapping, which requires collections of germplasm instead of biparental populations, has also been developed as a method for fi nding genes underlying quantitative traits (Hästbacka et al., 1992; Lazzeroni, 1997; Yu et al., 2006).”