PT  - JOURNAL ARTICLE
AU  - Yang-Jun Wen
AU  - Hanwen Zhang
AU  - Jin Zhang
AU  - Jian-Ying Feng
AU  - Bo Huang
AU  - Jim M. Dunwell
AU  - Yuan-Ming Zhang
AU  - Rongling Wu
TI  - A fast multi-locus random-SNP-effect EMMA for genome-wide association studies
AID  - 10.1101/077404
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 077404
4099  - http://biorxiv.org/content/early/2016/09/26/077404.short
4100  - http://biorxiv.org/content/early/2016/09/26/077404.full
AB  - Although the mixed linear model (MLM) such as efficient mixed model association (EMMA), has been widely used in genome-wide association studies (GWAS), relatively little is known about fast and efficient algorithms to implement multi-locus GWAS. To address this issue, we report a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA). In this method, a new matrix transformation was constructed to obtain a new genetic model that includes only quantitative trait nucleotide (QTN) variation and normal residual error; letting the number of nonzero eigenvalues be one and fixing the polygenic-to-residual variance ratio was used to increase computing speed. All the putative QTNs with the ≤0.005 P-values in the first step of the new method were included in one multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection, model fit and robustness, has less bias in QTN effect estimation, and requires less running time than the current single- and multi-locus methodologies for GWAS, such as E-BAYES, SUPER, EMMA, CMLM and ECMLM. Therefore, FASTmrEMMA provides an alternative for multi-locus GWAS.