title: Statistical challenges in analyzing methylation and long-range chromosomal interaction data.
abstract: With the rapid development of high throughput technologies such as array and next generation sequencing (NGS), genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Qin
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
authors.forename: Ben
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
authors.forename: Karen N
authors.initials: KN
authors.lastname: Conneely
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Wu
authors.affiliation: Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.affiliation: Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.
authors.forename: Deepak
authors.initials: D
authors.lastname: Ayyala
authors.affiliation: Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261 USA.
authors.forename: Yongseok
authors.initials: Y
authors.lastname: Park
authors.affiliation: Department of Molecular Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
authors.forename: Victor X
authors.initials: VX
authors.lastname: Jin
authors.affiliation: Department of Mathematics & Statistics, Texas Tech University, Lubbock, TX 79409, USA.
authors.forename: Fangyuan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.
authors.forename: Han
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.
authors.forename: Shili
authors.initials: S
authors.lastname: Lin