2005
DOI: 10.1038/ng1702
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A unified mixed-model method for association mapping that accounts for multiple levels of relatedness

Abstract: As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples:… Show more

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Cited by 3,525 publications
(3,617 citation statements)
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References 25 publications
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“…Numerous research studies have been implemented for GWAS using the mixed model approach that accounts for population structure and pair-wise kinship, initially described by Yu et al (2006). For this study, we evaluated the inclusion of population structure fixed effects in the model.…”
Section: Discussionmentioning
confidence: 99%
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“…Numerous research studies have been implemented for GWAS using the mixed model approach that accounts for population structure and pair-wise kinship, initially described by Yu et al (2006). For this study, we evaluated the inclusion of population structure fixed effects in the model.…”
Section: Discussionmentioning
confidence: 99%
“…The GWA function applies a mixed-linear model that can account for both population structure and markerbased kinship, denoted by K, originally described by Yu et al (2006). The model used for association analysis was Y = mean ?…”
Section: Association Analysismentioning
confidence: 99%
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“…Marker-based estimates have been combined with phenotypic observations to obtain heritability estimates for natural populations (Ritland 1996b;Thomas and Hill 2000). More recently, Yu et al (2006) incorporated a marker-based relationship matrix into a mixed model approach for association mapping. The markerbased relatedness among individuals effectively accounted for hidden levels of relatedness that otherwise resulted in spurious associations between genetic markers and quantitative traits (Yu et al 2006).…”
Section: Introductionmentioning
confidence: 99%
“…More recently, Yu et al (2006) incorporated a marker-based relationship matrix into a mixed model approach for association mapping. The markerbased relatedness among individuals effectively accounted for hidden levels of relatedness that otherwise resulted in spurious associations between genetic markers and quantitative traits (Yu et al 2006). In addition, including known relationships between parents of multiple mapping populations generally increases the power to detect and map QTL in linkage analysis (Bink et al 2002).…”
Section: Introductionmentioning
confidence: 99%