title: Probabilistic natural mapping of gene-level tests for genome-wide association studies.
abstract: Genome-wide association studies (GWASs) generally focus on a single marker, which limits the elucidation of the genetic architecture of complex traits. Herein, we present a new computational framework, termed probabilistic natural mapping (PALM), for performing gene-level association tests. PALM robustly reveals the inherent genomic structures of genes and generates feature representations that can be seamlessly incorporated into conventional statistic tests. Our approach substantially improves the effectiveness of uncovering associations derived from a subgroup of variants with weak effects, which represents a known challenge associated with existing methods. We applied PALM in a gastric cancer GWAS and identified two additional gastric cancer-associated susceptibility genes, NOC3L and RUNDC2A. The robust susceptibility discoveries of PALM are widely supported by existing studies from other biological perspectives. PALM will be useful for further GWAS analytical strategies that use gene-level analyses.
authors.affiliation: Department of Automation, Tsinghua University, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Bao
authors.affiliation: School of Pharmacy, University of California, San Francisco, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Deng
authors.affiliation: Department of Environmental Genomics, Nanjing Medical University, China.
authors.forename: Mulong
authors.initials: M
authors.lastname: Du
authors.affiliation: Department of Automation, Tsinghua University, China.
authors.forename: Zhiquan
authors.initials: Z
authors.lastname: Ren
authors.affiliation: School of Economics and Wang Yanan Institute for Studies in Economics, Xiamen University, China.
authors.forename: Qingzhao
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Boston University, USA.
authors.forename: Yanyu
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Department of Automation, Tsinghua University, China.
authors.forename: Jinli
authors.initials: J
authors.lastname: Suo
authors.affiliation: Department of Genetic Toxicology, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, and a PI in Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center For Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
authors.forename: Zhengdong
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Genetic Toxicology, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, and a PI in Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center For Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
authors.forename: Meilin
authors.initials: M
authors.lastname: Wang
authors.affiliation: Department of Automation, Tsinghua University, China.
authors.forename: Qionghai
authors.initials: Q
authors.lastname: Dai
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D055106:Genome-Wide Association Study; D023281:Genomics; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D013274:Stomach Neoplasms / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor