A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.
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: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.
Yu
Jianming
J
Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA.
Pressoir
Gael
G
Briggs
William H
WH
Vroh Bi
Irie
I
Yamasaki
Masanori
M
Doebley
John F
JF
McMullen
Michael D
MD
Gaut
Brandon S
BS
Nielsen
Dahlia M
DM
Holland
James B
JB
Kresovich
Stephen
S
Buckler
Edward S
ES
D015870:Gene Expression; D005821:Genetic Techniques; D014644:Genetic Variation; D040941:Heredity / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D012107:Research Design; D003313:Zea mays / Q000235:genetics*