Generalized Genomic Distance–Based Regression Methodology for Multilocus Association Analysis

Wessel, Jennifer; Schork, Nicholas J.

doi:10.1086/508346

Cited by 162 publications

(200 citation statements)

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“…Foremost, DAWGSS are limited to describing statistics that have an additive effect across the SNVs. Therefore, rank-based approaches, 7 methods that allow for interactions, 6 methods with non-additive effects, 18 and methods that compare all subsets of SNVs 33 are outside of the DAWGSS framework. However, we believe these limitations have minimal practical implications.…”

Section: Discussionmentioning

confidence: 99%

“…9 Numerous statistical tests that can search for these regional associations have already been introduced, developed, and compared. 7,[10][11][12][13][14][15][16][17][18][19] Our three goals in this paper are to (1) Unify (2) Identify, and (3) Modify association tests for rare variants. First, we introduce a simple statistical framework and show that the majority of rare-variant association tests can be reformulated within this framework.…”

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Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation

Ferguson

Wheeler

et al. 2012

Eur J Hum Genet

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With recent advances in sequencing, genotyping arrays, and imputation, GWAS now aim to identify associations with rare and uncommon genetic variants. Here, we describe and evaluate a class of statistics, generalized score statistics (GSS), that can test for an association between a group of genetic variants and a phenotype. GSS are a simple weighted sum of single-variant statistics and their cross-products. We show that the majority of statistics currently used to detect associations with rare variants are equivalent to choosing a specific set of weights within this framework. We then evaluate the power of various weighting schemes as a function of variant characteristics, such as MAF, the proportion associated with the phenotype, and the direction of effect. Ultimately, we find that two classical tests are robust and powerful, but details are provided as to when other GSS may perform favorably. The software package CRaVe is available at our website (http://dceg.cancer.gov/bb/tools/crave). European Journal of Human Genetics (2013) 21, 680-686; doi:10.1038/ejhg.2012.220; published online 24 October 2012Keywords: rare variants; score test; GWAS; association test INTRODUCTION The search for rare variants associated with common diseases, and traits in general, has already started. [1][2][3][4][5] As these variants are rare, most studies will be inadequately powered to detect an association with any single variant. [6][7][8] When only a handful of minor alleles are observed for any single-nucleotide variant (SNV), obtaining statistical significance, especially at traditional genome wide levels of 10 À8 , can be near impossible. Therefore, instead of trying to identify associations with individual variants, the goal has been to identify associations with a group of rare variants in a shared region (eg, exons, genes) or pathway, effectively increasing power by pooling information across SNVs. 9 Numerous statistical tests that can search for these regional associations have already been introduced, developed, and compared. 7,[10][11][12][13][14][15][16][17][18][19] Our three goals in this paper are to (1) Unify (2) Identify, and (3) Modify association tests for rare variants. First, we introduce a simple statistical framework and show that the majority of rare-variant association tests can be reformulated within this framework. Second, we show that within this framework, we can easily identify the relationship between a statistic's performance and the genetic characteristics of the tested SNVs, such as the proportion of SNVs associated with an outcome, direction of effects, and the relationship between effect size and MAF. Third, we show that the standard test statistics can be further tailored to the specifics of a given study or an investigator's prior beliefs.To achieve our objective of unification, we first revisit the standard association test for a single uncommon SNV. The standard approach would be to divide study participants into two groups, those with and

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation

Ferguson

Wheeler

et al. 2012

Eur J Hum Genet

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“…Wessel and Schork (2006) and Zapala and Schork (2012) introduced the method to applications in genetics and genomics. For a single trait, it is closely related to kernel methods (Schaid et al 2005;Pan 2011).…”

Section: Appendix B: Spu(22) and Mdmrmentioning

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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“…The first approach designs a test statistic that summarizes all genetic variation in the region and assesses its association with the outcome. [3][4][5][6][7][8][9][10][11] The second approach uses an unsupervised dimension reduction procedure, such as principal component (PC) analysis, to select a proportion of genetic variation (contained in either a subset of SNPs or selected PCs) without referring to their association with the outcome, and then relates the selected components to the outcome. [12][13][14][15] The third approach employs a supervised variable selection (SVS) procedure to identify a subset of variables that are most relevant to the outcome and then designs a test statistic based on the selected variables.…”

Section: Introductionmentioning

confidence: 99%

A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

et al. 2013

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As increasing evidence suggests that multiple correlated genetic variants could jointly influence the outcome, a multilocus test that aggregates association evidence across multiple genetic markers in a considered gene or a genomic region may be more powerful than a single-marker test for detecting susceptibility loci. We propose a multilocus test, AdaJoint, which adopts a variable selection procedure to identify a subset of genetic markers that jointly show the strongest association signal, and defines the test statistic based on the selected genetic markers. The P-value from the AdaJoint test is evaluated by a computationally efficient algorithm that effectively adjusts for multiple-comparison, and is hundreds of times faster than the standard permutation method. Simulation studies demonstrate that AdaJoint has the most robust performance among several commonly used multilocus tests. We perform multilocus analysis of over 26 000 genes/regions on two genome-wide association studies of pancreatic cancer. Compared with its competitors, AdaJoint identifies a much stronger association between the gene CLPTM1L and pancreatic cancer risk (6.0 Â 10 À8 ), with the signal optimally captured by two correlated single-nucleotide polymorphisms (SNPs). Finally, we show AdaJoint as a powerful tool for mapping cis-regulating methylation quantitative trait loci on normal breast tissues, and find many CpG sites whose methylation levels are jointly regulated by multiple SNPs nearby.

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Generalized Genomic Distance–Based Regression Methodology for Multilocus Association Analysis

Cited by 162 publications

References 51 publications

Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation

Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation

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

A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

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