Quantitative trait loci identification for brain endophenotypes via new additive model with random networks

Xiaoqian Wang, Hong Chen, Jingwen Yan, Kwangsik Nho, Shannon L. Risacher, Andrew Saykin, Li Shen, Heng Huang

Research output: Contribution to journalArticle

Abstract

Motivation The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs. Availability and implementation An executable is available at https://github.com/littleq1991/additive-FNNRW.

Original languageEnglish (US)
Pages (from-to)i866-i874
JournalBioinformatics
Volume34
Issue number17
DOIs
StatePublished - Sep 1 2018

Fingerprint

Endophenotypes
Quantitative Trait Loci
Additive Models
Random Networks
Alzheimer's Disease
Single nucleotide Polymorphism
Brain
Identification (control systems)
Nucleotides
Polymorphism
Single Nucleotide Polymorphism
Alzheimer Disease
High Throughput
Computational Learning Theory
Throughput
Neuroimaging
Dependent Observations
Generalization Error
Genetic Variation
Genotype

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Quantitative trait loci identification for brain endophenotypes via new additive model with random networks. / Wang, Xiaoqian; Chen, Hong; Yan, Jingwen; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew; Shen, Li; Huang, Heng.

In: Bioinformatics, Vol. 34, No. 17, 01.09.2018, p. i866-i874.

Research output: Contribution to journalArticle

Wang, Xiaoqian ; Chen, Hong ; Yan, Jingwen ; Nho, Kwangsik ; Risacher, Shannon L. ; Saykin, Andrew ; Shen, Li ; Huang, Heng. / Quantitative trait loci identification for brain endophenotypes via new additive model with random networks. In: Bioinformatics. 2018 ; Vol. 34, No. 17. pp. i866-i874.
@article{df8df54785f54481a3bd3417fd057942,
title = "Quantitative trait loci identification for brain endophenotypes via new additive model with random networks",
abstract = "Motivation The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs. Availability and implementation An executable is available at https://github.com/littleq1991/additive-FNNRW.",
author = "Xiaoqian Wang and Hong Chen and Jingwen Yan and Kwangsik Nho and Risacher, {Shannon L.} and Andrew Saykin and Li Shen and Heng Huang",
year = "2018",
month = "9",
day = "1",
doi = "10.1093/bioinformatics/bty557",
language = "English (US)",
volume = "34",
pages = "i866--i874",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "17",

}

TY - JOUR

T1 - Quantitative trait loci identification for brain endophenotypes via new additive model with random networks

AU - Wang, Xiaoqian

AU - Chen, Hong

AU - Yan, Jingwen

AU - Nho, Kwangsik

AU - Risacher, Shannon L.

AU - Saykin, Andrew

AU - Shen, Li

AU - Huang, Heng

PY - 2018/9/1

Y1 - 2018/9/1

N2 - Motivation The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs. Availability and implementation An executable is available at https://github.com/littleq1991/additive-FNNRW.

AB - Motivation The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs. Availability and implementation An executable is available at https://github.com/littleq1991/additive-FNNRW.

UR - http://www.scopus.com/inward/record.url?scp=85054130749&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054130749&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bty557

DO - 10.1093/bioinformatics/bty557

M3 - Article

C2 - 30423101

AN - SCOPUS:85054130749

VL - 34

SP - i866-i874

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 17

ER -