title: Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data.
abstract: Detecting disease-associated genomic outcomes is one of the key steps in precision medicine research. Cutting-edge high-throughput technologies enable researchers to unbiasedly test if genomic outcomes are associated with disease of interest. However, these technologies also include the challenges associated with the analysis of genome-wide data. Two big challenges are (1) how to reduce the effects of technical noise; and (2) how to handle the curse of dimensionality (i.e., number of variables are way larger than the number of samples). To tackle these challenges, we propose a constrained mixture of Bayesian hierarchical models (MBHM) for detecting disease-associated genomic outcomes for data obtained from paired/matched designs. Paired/matched designs can effectively reduce effects of confounding factors. MBHM does not involve multiple testing, hence does not have the problem of the curse of dimensionality. It also could borrow information across genes so that it can be used for whole genome data with small sample sizes.
authors.affiliation: School of Mathematical Sciences, Zhejiang University, HongZhou, Zhejiang, China.
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Li
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, United States of America.
authors.forename: Jarrett
authors.initials: J
authors.lastname: Morrow
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, United States of America.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Raby
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, United States of America.
authors.forename: Kelan
authors.initials: K
authors.lastname: Tantisira
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, United States of America.
authors.forename: Scott T
authors.initials: ST
authors.lastname: Weiss
authors.affiliation: School of Mathematical Sciences, Zhejiang University, HongZhou, Zhejiang, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Huang
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, United States of America.
authors.forename: Weiliang
authors.identifier: http://orcid.org/0000-0002-9155-6957
authors.initials: W
authors.lastname: Qiu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D008962:Models, Theoretical; D057285:Precision Medicine / Q000706:statistics & numerical data*