A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

Pan, Wei; Kwak, Il-Youp; Wei, Peng

doi:10.1016/j.ajhg.2015.05.018

Cited by 59 publications

(84 citation statements)

References 47 publications

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“…However, it may also dilute the association signal when the added CNVs are noise variables. One way to address this is through adaptive testing (Pan et al, 2015;Zhan et al, 2015a), which is of importance and warrants further investigation.…”

Section: Forming Cnvrmentioning

confidence: 99%

A novel copy number variants kernel association test with application to autism spectrum disorders studies

et al. 2016

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Motivation: Copy number variants (CNVs) have been implicated in a variety of neurodevelopmental disorders, including autism spectrum disorders, intellectual disability and schizophrenia. Recent advances in high-throughput genomic technologies have enabled rapid discovery of many genetic variants including CNVs. As a result, there is increasing interest in studying the role of CNVs in the etiology of many complex diseases. Despite the availability of an unprecedented wealth of CNV data, methods for testing association between CNVs and disease-related traits are still underdeveloped due to the low prevalence and complicated multi-scale features of CNVs. Results: We propose a novel CNV kernel association test (CKAT) in this paper. To address the low prevalence, CNVs are first grouped into CNV regions (CNVR). Then, taking into account the multiscale features of CNVs, we first design a single-CNV kernel which summarizes the similarity between two CNVs, and next aggregate the single-CNV kernel to a CNVR kernel which summarizes the similarity between two CNVRs. Finally, association between CNVR and disease-related traits is assessed by comparing the kernel-based similarity with the similarity in the trait using a score test for variance components in a random effect model. We illustrate the proposed CKAT using simulations and show that CKAT is more powerful than existing methods, while always being able to control the type I error. We also apply CKAT to a real dataset examining the association between CNV and autism spectrum disorders, which demonstrates the potential usefulness of the proposed method. Availability and Implementation: A R package to implement the proposed CKAT method is available at

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Section: Forming Cnvrmentioning

confidence: 99%

A novel copy number variants kernel association test with application to autism spectrum disorders studies

et al. 2016

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“…aSPUpath: The aSPUpath test is an adaptive test for gene sets extended from an adaptive gene-based test-the adaptive sum of powered score test (SPU) (Pan et al 2014(Pan et al , 2015. The essence of the aSPUpath test is that it adaptively aggregates signals at both the gene and the pathway level through a power-weighting scheme.…”

Section: A Class Of Tests and An Adaptive Testmentioning

confidence: 99%

“…Therefore, it directly tests association for a gene set and does not depend on genes outside the set. Some self-contained methods include the SNP-ratio test (SRT) (O'Dushlaine et al 2009), the sequence kernel association test (SKAT) (Wu et al 2010), PLINK-set (PLINK set-based test) (Purcell et al 2007), GRASS (gene set ridge regression in association studies) (Chen et al 2010), aSPUpath (adaptive pathway-based sum of powered score test) (Pan et al 2015), MAGMA (multi-marker analysis of genomic annotation) (de Leeuw et al 2015), and methods combining P-values. Comprehensive reviews and comparisons of existing methods have been offered by several authors (Wang et al 2010(Wang et al , 2011Atias et al 2013;Mooney et al 2014).…”

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confidence: 99%

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics

et al. 2016

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Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Despite remarkable success in uncovering many risk variants and providing novel insights into disease biology, genetic variants identified to date fail to explain the vast majority of the heritability for most complex diseases. One explanation is that there are still a large number of common variants that remain to be discovered, but their effect sizes are generally too small to be detected individually. Accordingly, gene set analysis of GWAS, which examines a group of functionally related genes, has been proposed as a complementary approach to single-marker analysis. Here, we propose a flexible and adaptive test for gene sets (FLAGS), using summary statistics. Extensive simulations showed that this method has an appropriate type I error rate and outperforms existing methods with increased power. As a proof of principle, through real data analyses of Crohn's disease GWAS data and bipolar disorder GWAS meta-analysis results, we demonstrated the superior performance of FLAGS over several state-of-the-art association tests for gene sets. Our method allows for the more powerful application of gene set analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.

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“…Pathway analysis: We extend the adaptive test for association analysis of a single trait and a pathway (i.e., a set of genes) (Pan et al 2015) to that of multiple traits and a pathway. The main idea is to allow adaptive weighting at the gene level, in addition to at the SNP and trait levels.…”

Section: An Adaptive Testmentioning

confidence: 99%

“…Muller and Peterson (1984) discussed the close relationships among four versions of MANOVA (i.e., with the Pillai-Bartlett trace, Hotelling-Lawley's trace, Wilk's l, and Roy's largest root), each of which can be written as a function of generalized canonical correlations (CCA). Hence the GEE-Score test is directly related to MANOVA and CCA.Pathway analysis: We extend the adaptive test for association analysis of a single trait and a pathway (i.e., a set of genes) (Pan et al 2015) to that of multiple traits and a pathway. The main idea is to allow adaptive weighting at the gene level, in addition to at the SNP and trait levels.…”

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confidence: 99%

Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data

Kim¹,

Zhang²,

Pan

2016

Genetics

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Testing for genetic association with multiple traits has become increasingly important, not only because of its potential to boost statistical power, but also for its direct relevance to applications. For example, there is accumulating evidence showing that some complex neurodegenerative and psychiatric diseases like Alzheimer's disease are due to disrupted brain networks, for which it would be natural to identify genetic variants associated with a disrupted brain network, represented as a set of multiple traits, one for each of multiple brain regions of interest. In spite of its promise, testing for multivariate trait associations is challenging: if not appropriately used, its power can be much lower than testing on each univariate trait separately (with a proper control for multiple testing). Furthermore, differing from most existing methods for single-SNP-multiple-trait associations, we consider SNP set-based association testing to decipher complicated joint effects of multiple SNPs on multiple traits. Because the power of a test critically depends on several unknown factors such as the proportions of associated SNPs and of traits, we propose a highly adaptive test at both the SNP and trait levels, giving higher weights to those likely associated SNPs and traits, to yield high power across a wide spectrum of situations. We illuminate relationships among the proposed and some existing tests, showing that the proposed test covers several existing tests as special cases. We compare the performance of the new test with that of several existing tests, using both simulated and real data. The methods were applied to structural magnetic resonance imaging data drawn from the Alzheimer's Disease Neuroimaging Initiative to identify genes associated with gray matter atrophy in the human brain default mode network (DMN). For genomewide association studies (GWAS), genes AMOTL1 on chromosome 11 and APOE on chromosome 19 were discovered by the new test to be significantly associated with the DMN. Notably, gene AMOTL1 was not detected by single SNP-based analyses. To our knowledge, AMOTL1 has not been highlighted in other Alzheimer's disease studies before, although it was indicated to be related to cognitive impairment. The proposed method is also applicable to rare variants in sequencing data and can be extended to pathway analysis.KEYWORDS adaptive association test; ADNI; default mode network; gene-based test; imaging genetics; multiple traits A LZHEIMER'S disease (AD) (MIM 104300) is the most common neurodegenerative disease, and every 67 sec, someone in the United States develops AD (Alzheimer's Association 2015a). Currently there is no cure for AD, and most cases are diagnosed in the late stage of the disease. It is projected that the number of Americans of age 65 years and older with AD will increase from 5.1 million in 2015 to 13.5 million in 2050, a growth from an estimated 11% of the U.S. senior population in 2015 to 16% in 2050, costing .$1.1 trillion in 2050 (Alzheimer's Association 2015b). To advance our...

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A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

Cited by 59 publications

References 47 publications

A novel copy number variants kernel association test with application to autism spectrum disorders studies

A novel copy number variants kernel association test with application to autism spectrum disorders studies

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics

Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data

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