Multilocus Random Mixed Linear Model for Genome-Wide Association
Study
Wenlong Ren,Shibo Wang and Yuanming Zhang
Conduct multi-locus random-SNP-effect mixed linear model analysis for genome-wide association study. First, each marker is scanned using random-SNP-effect mixed linear model method. Then, all the markers selected from the first step are included in the multi-locus model and their effects are estimated by an EM empirical Bayes method. No multiple test correction is needed.

Wenlong Ren,Shibo Wang,Bo Huang and Yuanming Zhang

Multilocus Random Mixed Linear Model for Genome-Wide Association
Study and Linkage Analysis
Wenlong Ren,Yuanli Ni Shibo Wang,Bo Huang and Yuanming Zhang
Conduct multi-locus random-SNP-effect mixed linear model analysis for genome-wide association study and linkage analysis. First, each position (or marker) on the genome is scanned using random-SNP-effect mixed linear model method. Then, all the markers (or putative QTL) selected from the first step are included in the multi-locus model and their effects are estimated by an EM empirical Bayes method. Bonferroni correction is replaced by a less stringent selection criterion for significance test.

Multi-Locus Random-SNP-Effect Mixed Linear Model for Genome-Wide
Association Studies and Linkage Analyses
Wenlong Ren,Yuanli Ni,Shibo Wang,Yangjun Wen,Bo Huang and Yuanming Zhang
Conduct multi-locus GWAS and multi-locus QTL mapping under the framework of random-SNP-effect mixed linear model (mrMLM). First, each position (or marker) on the genome is scanned by mrMLM algorithm. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers (or QTL) that are potentially associated with the trait are included in a multi-locus model, their effects are estimated by empirical Bayes and true QTN or QTL are identified by likelihood ratio test.

Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for
Genome-Wide Association Study
Zhang Ya-Wen, Li Pei, Ren Wen-Long, Ni Yuan-Li, and Zhang Yuan-Ming
Conduct multi-locus genome-wide association study under the framework of random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus model, their effects are estimated by empirical Bayes and true QTN are identified by likelihood ratio test.