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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #321684

Research Project: Development and Application of Genetic, Genomic, and Bioinformatic Resources in Maize

Location: Plant, Soil and Nutrition Research

Title: A super powerful method for genome wide association study

Author
item WANG, QISHAN - Shanghai Jiaotong University
item TIAN, FENG - China Agricultural University
item PAN, YUCHUN - Shanghai Jiaotong University
item Buckler, Edward - Ed
item ZHANG, ZHIWU - Cornell University - New York

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2014
Publication Date: 7/23/2014
Citation: Wang, Q., Tian, F., Pan, Y., Buckler IV, E.S., Zhang, Z. 2014. A super powerful method for genome wide association study. PLoS One. 9(9):e107684.

Interpretive Summary: We developed a powerful and fast Genome-Wide Association Studies (GWAS) model. This model uses Single-Nucleotide Polymorphism (SNP) subsets to improve on previous GWAS models which either required too much computer power to be useful or gave off too many false positive results. GWAS is used to help identify genes associate with specific traits. The resulting model was made publically available within the GAPIT software package.

Technical Abstract: Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.