TY - JOUR
T1 - Genotype-phenotype association study via new multi-task learning model
AU - Huo, Zhouyuan
AU - Shen, Dinggang
AU - Huang, Heng
N1 - Funding Information:
∗Corresponding Author. This work was partially supported by U.S. NIH R01 AG049371, NSF IIS 1302675, IIS 1344152, DBI 1356628, IIS 1619308, IIS 1633753 ©c 2017 The Authors. Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License.
Funding Information:
This work was partially supported by U.S. NIH R01 AG049371, NSF IIS 1302675, IIS 1344152, DBI 1356628, IIS 1619308, IIS 1633753.
Publisher Copyright:
© 2017 The Authors.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Multi-task learning
KW - Quantitative trait loci
KW - Quantitative traits (QTs)
KW - Single nucleotide polymorphisms (SNPs)
UR - http://www.scopus.com/inward/record.url?scp=85048468257&partnerID=8YFLogxK
U2 - 10.1142/9789813235533_0033
DO - 10.1142/9789813235533_0033
M3 - Conference article
C2 - 29218896
AN - SCOPUS:85048468257
SN - 2335-6936
VL - 0
SP - 353
EP - 364
JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
IS - 212669
T2 - 23rd Pacific Symposium on Biocomputing, PSB 2018
Y2 - 3 January 2018 through 7 January 2018
ER -