Prioritizing risk pathways: a novel association approach to searching for disease pathways fusing SNPs and pathways

Chen, Lina; Zhang, Liangcai; Yan, Zhao; Xu, Liangde; Shang, Yukui; Wang, Qian; Li, Wan; Wang, Hong; Li, Xia

doi:10.1093/bioinformatics/btn613

Cited by 51 publications

(39 citation statements)

References 18 publications

(19 reference statements)

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“…Chen 58 Two step SC PRP: gene association test based on max risk statistic; followed by mean risk for GS. Holmans 13 Two step C ALIGATOR: Fisher's exact testing using SNP with minimum P-value for the gene-level association.…”

Section: Analytical Approachesmentioning

confidence: 99%

Gene set analysis of SNP data: benefits, challenges, and future directions

2011

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The last decade of human genetic research witnessed the completion of hundreds of genome-wide association studies (GWASs). However, the genetic variants discovered through these efforts account for only a small proportion of the heritability of complex traits. One explanation for the missing heritability is that the common analysis approach, assessing the effect of each singlenucleotide polymorphism (SNP) individually, is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) is one of several approaches that may contribute to the discovery of additional genetic risk factors for complex traits. Complex phenotypes are thought to be controlled by networks of interacting biochemical and physiological pathways influenced by the products of sets of genes. By assessing the overall evidence of association of a phenotype with all measured variation in a set of genes, GSA may identify functionally relevant sets of genes corresponding to relevant biomolecular pathways, which will enable more focused studies of genetic risk factors. This approach may thus contribute to the discovery of genetic variants responsible for some of the missing heritability. With the increased use of these approaches for the secondary analysis of data from GWAS, it is important to understand the different GSA methods and their strengths and weaknesses, and consider challenges inherent in these types of analyses. This paper provides an overview of GSA, highlighting the key challenges, potential solutions, and directions for ongoing research.

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Section: Analytical Approachesmentioning

confidence: 99%

Gene set analysis of SNP data: benefits, challenges, and future directions

2011

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“…After filtering, 789 articles were retained. Furthermore, to analyze disease-related pathways, there were seven studies about PBA for BD GWAS data collected (20,(23)(24)(25)(26)(27)(28). In all, 796 studies were included in BDgene.…”

Section: Literature Search and Data Extractionmentioning

confidence: 99%

BDgene: A Genetic Database for Bipolar Disorder and Its Overlap With Schizophrenia and Major Depressive Disorder

Chang¹,

Gao²,

Zhao³

et al. 2013

Biological Psychiatry

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“…2 Pathway analysis typically tests the association of a predefined set of related genes, which are often defined by biological knowledge. 3 Although pathway analysis methods have been developed and successfully applied to association studies of common variants, [4][5][6][7][8][9][10][11][12][13][14][15][16][17] the statistical methods for pathway-based association analysis of rare variants have not been well developed. [18][19][20][21] The current methods for pathway-based association analysis of rare variants are classified into two approaches.…”

Section: Introductionmentioning

confidence: 99%

Pathway analysis with next-generation sequencing data

et al. 2014

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Although pathway analysis methods have been developed and successfully applied to association studies of common variants, the statistical methods for pathway-based association analysis of rare variants have not been well developed. Many investigators observed highly inflated false-positive rates and low power in pathway-based tests of association of rare variants. The inflated false-positive rates and low true-positive rates of the current methods are mainly due to their lack of ability to account for gametic phase disequilibrium. To overcome these serious limitations, we develop a novel statistic that is based on the smoothed functional principal component analysis (SFPCA) for pathway association tests with next-generation sequencing data. The developed statistic has the ability to capture position-level variant information and account for gametic phase disequilibrium. By intensive simulations, we demonstrate that the SFPCA-based statistic for testing pathway association with either rare or common or both rare and common variants has the correct type 1 error rates. Also the power of the SFPCA-based statistic and 22 additional existing statistics are evaluated. We found that the SFPCA-based statistic has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the SFPCA-based statistic is applied to pathway analysis of exome sequencing data in the early-onset myocardial infarction (EOMI) project. We identify three pathways significantly associated with EOMI after the Bonferroni correction. In addition, our preliminary results show that the SFPCA-based statistic has much smaller P-values to identify pathway association than other existing methods.

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Prioritizing risk pathways: a novel association approach to searching for disease pathways fusing SNPs and pathways

Cited by 51 publications

References 18 publications

Gene set analysis of SNP data: benefits, challenges, and future directions

Gene set analysis of SNP data: benefits, challenges, and future directions

BDgene: A Genetic Database for Bipolar Disorder and Its Overlap With Schizophrenia and Major Depressive Disorder

Pathway analysis with next-generation sequencing data

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