Sparse models for correlative and integrative analysis of imaging and genetic data

Lin, Dongdong; Cao, Hongbao; Calhoun, Vince D.; Wang, Yu Ping

doi:10.1016/j.jneumeth.2014.09.001

Cited by 47 publications

(36 citation statements)

References 84 publications

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“…However, existing methods use imaging endophenotypes as GWAS traits to directly identify endophenotypes-associated single nucleotide polymorphisms (SNPs) or genes (e.g., Lin et al 2014; Shen et al 2014; Zhu et al 2014; Huang et al 2015; Lu et al 2017; Tao et al 2017, and references therein). Since the identified SNPs or genes may or may not be associated with the disease, e.g.…”

Section: Introductionmentioning

confidence: 99%

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

Pan

2017

NeuroImage

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A new and powerful approach, called imaging-wide association study (IWAS), is proposed to integrate imaging endophenotypes with GWAS to boost statistical power and enhance biological interpretation for GWAS discoveries. IWAS extends the promising transcriptome-wide association study (TWAS) from using gene expression endophenotypes to using imaging and other endophenotypes with a much wider range of possible applications. As illustration, we use gray-matter volumes of several brain regions of interest (ROIs) drawn from the ADNI-1 structural MRI data as imaging endophenotypes, which are then applied to the individual-level GWAS data of ADNI-GO/2 and a large meta-analyzed GWAS summary statistics dataset (based on about 74000 individuals), uncovering some novel genes significantly associated with Alzheimer’s disease (AD). We also compare the performance of IWAS with TWAS, showing much larger numbers of significant AD-associated genes discovered by IWAS, presumably due to the stronger link between brain atrophy and AD than that between gene expression of normal individuals and the risk for AD. The proposed IWAS is general and can be applied to other imaging endophenotypes, and GWAS individual-level or summary association data.

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Section: Introductionmentioning

confidence: 99%

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

Pan

2017

NeuroImage

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“…These observations motivate us to seek a subset of low-rank variables in SNPs. On the other hand, the previous studies (e.g., [18], [21], [50], [51]) have manifested that the low-rank assumption in data representation helps make the resulting regression model more accurate.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…As a gene sequence includes a number of SNPs, there may be high correlation among SNPs [21], [51]. Moreover, while there are thousands of SNPs, some of them may not be associated with neuroimaging phenotypes.…”

Section: Proposed Methodsmentioning

confidence: 99%

Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers

Zhu

Suk

Huang

et al. 2017

IEEE Trans. Big Data

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In this paper, we propose a novel sparse regression method for Brain-Wide and Genome-Wide association study. Specifically, we impose a low-rank constraint on the weight coefficient matrix and then decompose it into two low-rank matrices, which find relationships in genetic features and in brain imaging features, respectively. We also introduce a sparse acyclic digraph with sparsity-inducing penalty to take further into account the correlations among the genetic variables, by which it can be possible to identify the representative SNPs that are highly associated with the brain imaging features. We optimize our objective function by jointly tackling low-rank regression and variable selection in a framework. In our method, the low-rank constraint allows us to conduct variable selection with the low-rank representations of the data; the learned low-sparsity weight coefficients allow discarding unimportant variables at the end. The experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method could select the important SNPs to more accurately estimate the brain imaging features than the state-of-the-art methods.

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“…Moreover, these neurological disorders often are regarded as the end result of abnormal trajectories of brain development, which may be caused by the additive and interactive effects of perhaps hundreds of risk genes and multiple environmental risk factors, each with small individual effects. A promising approach to overcome such difficulties is to discover genetic factors associated with brain changes that may lead to key insights in neurological disorders and our understanding of the origins of these conditions (Hibar et al, 2011; Chen et al, 2012; Ge et al, 2012; Thompson et al, 2013; Medland et al, 2014; Ge et al, 2015a,b; Hibar et al, 2015; Lin et al, 2014a; Huang et al, 2015; Tao et al, 2017). Such efforts may inspire new approaches to urgently needed preventions, diagnoses, and treatments.…”

Section: Introductionmentioning

confidence: 99%

FGWAS: Functional genome wide association analysis

et al. 2017

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Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants and the gene-environmental interactions influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimers Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

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Sparse models for correlative and integrative analysis of imaging and genetic data

Cited by 47 publications

References 84 publications

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers

FGWAS: Functional genome wide association analysis

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