Graph-structured Sparse Mixed Models for Genetic Association with Confounding Factors Correction

Haohan Wang, Changpeng Lu, Wei Wu, Eric P. Xing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Genome-Wide Association Study (GWAS) plays an essential role in understanding human genetics. While various methods have been introduced to increase the signals of GWAS with consideration of population stratification, polygenicity, or pleiotropy. There seems no existing methods that can consdier these three different aspects of genetic association studies together. In this paper, we introduce a new set of models that can utilize the relatedness of available phenotypes to help improve the signals regarding pleiotropy, calculate multivariate coefficients corresponds to polygenicity, and correct population stratification through modelling random effects. We first propose the sparse graph-structured linear mixed model (sGLMM). Then the tree-guided sparse linear mixed model (TgSLMM) has further put forward to explore how specifically clusters are. Our method turns out to outperform other existing approaches after simulation experiments and be capable of exploring the correct genetic association and scales to the large dataset like human genome. Further, we validate, compare and use the effectiveness of both sGLMM and TgSLMM in the real-world genomic dataset on Human Alzheimer's Disease discovered by our model, and justify a few of the most important genetic loci. Overlapping SNPs implies that association between each pair of traits follows only one path and is acyclic, which more corresponds to tree structure.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages298-302
Number of pages5
ISBN (Electronic)9781728118673
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: Nov 18 2019Nov 21 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/TerritoryUnited States
CitySan Diego
Period11/18/1911/21/19

Keywords

  • genome association
  • mixed model
  • variant

ASJC Scopus subject areas

  • Biochemistry
  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Health Informatics
  • Pharmacology (medical)
  • Public Health, Environmental and Occupational Health

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