title: Longitudinal data analysis for rare variants detection with penalized quadratic inference function.
abstract: Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.
authors.affiliation: Shanxi Medical University, Department of Health Statistics, Taiyuan, 030001, China.
authors.forename: Hongyan
authors.initials: H
authors.lastname: Cao
authors.affiliation: North University of China, School of Sport and Physical Education, Taiyuan, 030051, China.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Li
authors.affiliation: Hebei Medical University, Department of Epidemiology and Health Statistics, Shijiazhuang, 050017, China.
authors.forename: Haitao
authors.initials: H
authors.lastname: Yang
authors.affiliation: Shanxi Medical University, Department of Health Statistics, Taiyuan, 030001, China. cui@stt.msu.edu.|Michigan State University, Department of Statistics and Probability, East Lansing, MI, 48824, USA. cui@stt.msu.edu.
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Shanxi Medical University, Department of Health Statistics, Taiyuan, 030001, China. sxmuzyb@126.com.
authors.forename: Yanbo
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D005820:Genetic Testing; D014644:Genetic Variation; D006801:Humans; D006973:Hypertension / Q000235:genetics; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012907:Smoking