Lingling An

Lingling An

Associate Professor, Agricultural-Biosystems Engineering
Associate Professor, Public Health
Associate Professor, Statistics-GIDP
Associate Professor, BIO5 Institute
Member of the General Faculty
Member of the Graduate Faculty
Primary Department
Department Affiliations
Contact
(520) 621-1248

Research Interest

Lingling An, PhD, conducts research in the interdisciplinary boundaries of many fields such as statistical sciences, biological and medical sciences, genomics and genetics. Her statistical group's major research interests include development and application of statistical and computational methods for analysis of high-dimensional genomic/genetic, metagenomic/ metatranscriptomic, and epigenomic data. The overlying vision is to develop rigorous, timely and useful statistical and computational methodologies to help biologists/geneticists to ask, answer, and disseminate biologically interesting information in the quest to understand the ultimate function of DNA and gene network.

Publications

Luo, D., Ziebell, S., & An, L. (2017). An Informative Approach on Differential Abundance Analysis for Time-course Metagenomic Sequencing Count Data. Bioinformatics. doi:https://doi.org/10.1093/bioinformatics/btw828
An, L., Niu, Y. S., Hao, N., & An, L. -. (2011). Detection of rare functional variants using group ISIS. BMC proceedings, 5 Suppl 9.

Genome-wide association studies have been firmly established in investigations of the associations between common genetic variants and complex traits or diseases. However, a large portion of complex traits and diseases cannot be explained well by common variants. Detecting rare functional variants becomes a trend and a necessity. Because rare variants have such a small minor allele frequency (e.g., 0.05), detecting functional rare variants is challenging. Group iterative sure independence screening (ISIS), a fast group selection tool, was developed to select important genes and the single-nucleotide polymorphisms within. The performance of the group ISIS and group penalization methods is compared for detecting important genes in the Genetic Analysis Workshop 17 data. The results suggest that the group ISIS is an efficient tool to discover genes and single-nucleotide polymorphisms associated to phenotypes.