Calmodulin-Like (CML) Gene Family in Medicago truncatula: Genome-Wide Identification, Characterization and Expression Analysis
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Authors: Sun, Qiguo; Yu, Shuhan; Guo, Zhenfei
Abstract
Calcium is an important second messenger in mediating adaptation responses of plants to abiotic and biotic stresses. Calmodulin-like (CML) protein is an important calcium-signaling protein that can sense and decode Ca2+ signal in plants. Medicago truncatula is a model legume plant; however, investigations of MtCML proteins are limited. Using genome analysis and BLAST database searches, fifty MtCML proteins that possess EF-hand motifs were identified. Phylogenetic analysis showed that CML homologs between M. truncatula, Arabidopsis thaliana and Oryza sativa shared close relationships. Gene structure analysis revealed that these MtCML genes contained one to four conserved EF-hand motifs. All MtCMLs are localized to eight chromosomes and underwent gene duplication. In addition, MtCML genes were differentially expressed in different tissues of M. truncatula. Cis-acting elements in promoter region and expression analysis revealed the potential response of MtCML protein to abiotic stress and hormones. The results provide a basis of further functional research on the MtCML gene family and facilitate their potential use for applications in the genetic improvement on M. truncatula in drought, cold and salt stress environments.
Empirical and conditional likelihoods for two-phase studies
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
Authors: Che, Menglu; Lawless, Jerald F.; Han, Peisong
Abstract
Two-phase, response-dependent sampling is often used in regression settings that involve expensive covariate measurements. Conditional maximum likelihood (CML) is an attractive approach in many cases as it avoids modelling of the covariate distribution, unlike full maximum likelihood. Scott & Wild (2011) introduced an augmented CML approach which is semi-parametric efficient in certain settings with a discrete response variable. We consider general regression models and show the Scott-Wild estimator of covariate effects has the same asymptotic efficiency as two empirical likelihood estimators, and that these estimators dominate the CML estimator. We compare the efficiencies of various estimators in simulation studies and illustrate the methodology in a two-phase genetics study.