Dataset Information

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

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. ?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 ?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.

SUBMITTER: Huo Z

PROVIDER: S-EPMC5890010 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Huo Zhouyuan Z Shen Dinggang D Huang Heng H

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20180101

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. ℓ2, 1-norm, leading t ...[more]

PMID: 29218896

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Project description:Osteogenesis imperfecta (OI), mainly caused by structural abnormalities of type I collagen, is a hereditary rare disease characterized by increased bone fragility and reduced bone mass. Clinical manifestations of OI mostly include multiple repeated bone fractures, thin skin, blue sclera, hearing loss, cardiovascular and pulmonary system abnormalities, triangular face, dentinogenesis imperfecta (DI), and walking with assistance. Currently, 20 causative genes with 18 subtypes have been identified for OI, of them, variations in COL1A1 and COL1A2 have been demonstrated to be major causative factors to OI. However, the complexity of the bone formation process indicates that there are potential new pathogenic genes associated with OI. To comprehensively explore the underlying mechanism of OI, we conducted association analysis between genotypes and phenotypes of OI diseases and found that mutations in COL1A1 and COL1A2 contributed to a large proportion of the disease phenotypes. We categorized the clinical phenotypes and the genotypes based on the variation types for those 155 OI patients collected from literature, and association study revealed that three phenotypes (bone deformity, DI, walking with assistance) were enriched in two variation types (the Gly-substitution missense and groups of frameshift, nonsense, and splicing variations). We also identified four novel variations (c.G3290A (p.G1097D), c.G3289C (p.G1097R), c.G3289A (p.G1097S), c.G3281A (p.G1094D)) in gene COL1A1 and two novel variations (c.G2332T (p.G778C), c.G2341T (p.G781C)) in gene COL1A2, which could potentially contribute to the disease. In addition, we identified several new potential pathogenic genes (ADAMTS2, COL5A2, COL8A1) based on the integration of protein-protein interaction and pathway enrichment analysis. Our study provides new insights into the association between genotypes and phenotypes of OI and novel information for dissecting the underlying mechanism of the disease.

Dataset Information

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

Publications

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

Similar Datasets

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