title: Statistical dissection of cyto-nuclear epistasis subject to genomic imprinting in line crosses.
abstract: Cytoplasm contains important metabolism reaction organelles such as mitochondria and chloroplast (in plant). In particular, mitochondria contains special DNA information which can be passed to offsprings through maternal gametes, and has been confirmed to play a pivotal role in nuclear activities. Experimental evidences have documented the importance of cyto-nuclear interactions in affecting important biological traits. While studies have also pointed out the role of interaction between imprinting nuclear DNA and cytoplasm, no statistical method has been developed to efficiently model such effect and further quantify its effect size. In this work, we developed an efficient statistical model for genome-wide estimating and testing the cytoplasmic effect, nuclear DNA imprinting effect as well as the interaction between them under reciprocal backcross and F2 designs derived from inbred lines. Parameters are estimated under maximum likelihood framework implemented with the EM algorithm. Extensive simulations show good performance in a variety of scenarios. The utility of the method is demonstrated by analyzing a published data set in an F2 family derived from C3H/HeJBir and C57BL/6 J mouse strains. Important cyto-nuclear interactions were identified. Our approach provides a quantitative framework for identifying and estimating cyto-nuclear interactions subject to genomic imprinting involved in the genetic control of complex traits.
authors.affiliation: Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America.
authors.forename: Tao
authors.initials: T
authors.lastname: He
authors.affiliation: Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America; Division of Medical Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Sa
authors.affiliation: Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America.
authors.forename: Ping-Shou
authors.initials: PS
authors.lastname: Zhong
authors.affiliation: Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America; Division of Medical Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Cui
mesh_terms: D000465:Algorithms; D000818:Animals; D002467:Cell Nucleus / Q000235:genetics*; D003198:Computer Simulation; D003433:Crosses, Genetic; D003593:Cytoplasm / Q000235:genetics*; D004843:Epistasis, Genetic; D005260:Female; D018392:Genomic Imprinting; D008297:Male; D051379:Mice; D008809:Mice, Inbred C3H; D008810:Mice, Inbred C57BL; D008957:Models, Genetic; D040641:Quantitative Trait Loci