Genotype-phenotype association study via new multi-task learning model

Zhouyuan Huo, Dinggang Shen, Heng Huang

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ 2,1 -norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ 2,1 -norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs.

Original languageEnglish
Pages (from-to)353-364
Number of pages12
JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Volume0
Issue number212669
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: 2018 Jan 32018 Jan 7

Keywords

  • Multi-task learning
  • Quantitative trait loci
  • Quantitative traits (QTs)
  • Single nucleotide polymorphisms (SNPs)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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