Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies

Wu, Michael C.; Kraft, Peter; Epstein, Michael P.; Taylor, Deanne; Chanock, Stephen J.; Hunter, David J.; Lin, Xihong

doi:10.1016/j.ajhg.2010.05.002

Cited by 548 publications

(805 citation statements)

References 49 publications

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“…Aggregate SNP methods reduce the total number of tests performed and increase power by taking advantage of the linkage disequilibrium (LD) across multiple SNPs (Wu et al 2010). By reducing the number of tests and increasing power, smaller sample sizes are required compared with traditional GWAS.…”

Section: Introductionmentioning

confidence: 99%

“…The IBS kernel models the aggregate variation using genetic similarity between individuals. The weighted kernel functions upweight rarer variants, whereas all SNPs are treated the same in the unweighted version (Wu et al 2010). We also considered a kernel function which allows for multiplicative SNP interactions within the gene region.…”

Section: Statistical Analysesmentioning

confidence: 99%

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Gene-based aggregate SNP associations between candidate AD genes and cognitive decline

Nettiksimmons

Tranah

Evans

et al. 2016

AGE

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Single nucleotide polymorphisms (SNPs) in and near ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, complement receptor 1 (CR1), EPHA1, EXOC3L2, FERMT2, HLA cluster (DRB5-DQA), INPP5D, MEF2C, MS4A cluster (MS4A3-MS4A6E), NME8, PICALM, PTK2B, SLC24A4, SORL1, and ZCWPW1 have been associated with Alzheimer's disease (AD) in large meta-analyses. We aimed to determine whether established AD-associated genes are associated with longitudinal cognitive decline by examining aggregate variation across these gene regions. In two single-sex cohorts of older, community-dwelling adults, we examined the association between SNPs in previously implicated gene regions and cognitive decline (age-adjusted person-specific cognitive slopes) using a Sequence Kernel Association Test (SKAT). In regions which showed aggregate significance, we examined the univariate association between individual AGE (2016) SNPs in the region and cognitive decline. Only two of the original AD-associated SNPs were significantly associated with cognitive decline in our cohorts. We identified significant aggregate-level associations between cognitive decline and the gene regions BIN1, CD33, CELF1, CR1, HLA cluster, and MEF2C in the allfemale cohort and significant associations with ABCA7, HLA cluster, MS4A6E, PICALM, PTK2B, SLC24A4, and SORL1 in the all-male cohort. We also identified a block of eight correlated SNPs in CD33 and several blocks of correlated SNPs in CELF1 that were significantly associated with cognitive decline in univariate analysis in the all-female cohort.

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Section: Introductionmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

Gene-based aggregate SNP associations between candidate AD genes and cognitive decline

Nettiksimmons

Tranah

Evans

et al. 2016

AGE

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“…MEGHA largely falls in the kernel machines framework (11), which subsumes the GCTA model as a special case and uses a variance component score test (12), known as sequence kernel association test (SKAT) (13)(14)(15), for efficient statistical inferences. MEGHA provides both magnitude estimates and significance measures of heritability with orders of magnitude less computational effort relative to GCTA, making it possible to analyze millions of phenotypes and develop sampling techniques that produce accurate inferences for Significance Practical tools for high-dimensional heritability-based screening are invaluable for prioritizing phenotypes for genetic studies with the dramatic expansion of available phenotypic data.…”

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confidence: 99%

Massively expedited genome-wide heritability analysis (MEGHA)

Nichols

Lee

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for largescale heritability-based screening and high-dimensional heritability profile construction.heritability | genome-wide complex trait analysis | imaging genetics | endophenotype | phenomics

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“…There is another class of methods that consider multiple SNPs close to each other (Wu et al, 2010(Wu et al, , 2011Listgarten et al, 2013). These problems are completely different and characterized by very different challenges.…”

Section: Discussionmentioning

confidence: 99%

Gene–Gene Interactions Detection Using a Two-stage Model

Wang

Sul

Snir

et al. 2015

Journal of Computational Biology

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Genome-wide association studies (GWAS) have discovered numerous loci involved in genetic traits. Virtually all studies have reported associations between individual single nucleotide polymorphisms (SNPs) and traits. However, it is likely that complex traits are influenced by interaction of multiple SNPs. One approach to detect interactions of SNPs is the brute force approach which performs a pairwise association test between a trait and each pair of SNPs. The brute force approach is often computationally infeasible because of the large number of SNPs collected in current GWAS studies. We propose a two-stage model, Threshold-based Efficient Pairwise Association Approach (TEPAA), to reduce the number of tests needed while maintaining almost identical power to the brute force approach. In the first stage, our method performs the single marker test on all SNPs and selects a subset of SNPs that achieve a certain significance threshold. In the second stage, we perform a pairwise association test between traits and pairs of the SNPs selected from the first stage. The key insight of our approach is that we derive the joint distribution between the association statistics of a single SNP and the association statistics of pairs of SNPs. This joint distribution allows us to provide guarantees that the statistical power of our approach will closely approximate the brute force approach. We applied our approach to the Northern Finland Birth Cohort data and achieved 63 times speedup while maintaining 99% of the power of the brute force approach.

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Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies

Cited by 548 publications

References 49 publications

Gene-based aggregate SNP associations between candidate AD genes and cognitive decline

Gene-based aggregate SNP associations between candidate AD genes and cognitive decline

Massively expedited genome-wide heritability analysis (MEGHA)

Gene–Gene Interactions Detection Using a Two-stage Model

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