Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models

DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS).

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“…Consistent with previous work [Fan et al, 2015a,2015b], choice of K b has little impact on performance (Supplementary Fig. S2).…”

Section: Methodssupporting

confidence: 88%

Across‐Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression

Zhang

Huang

et al. 2016

Genetic Epidemiology

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“…To estimate the GVFs X ci ðtÞ from the genotypes G ci , we use an ordinary linear square smoother. [16][17][18][19][20]42,43 Let ϕ k (t), k = 1, ⋯, K, be a series of K basis functions, such as the B-spline basis and Fourier basis functions. Denote ϕ(t) = (ϕ 1 (t), ⋯, ϕ K (t))′.…”

Section: Expansion Of Genetic Effectmentioning

confidence: 99%

“…10,[16][17][18][19][20][21][22][23][24][25][26][27][28][29] In most cases, it was shown that the functional regression test statistics perform better than sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C) of mixed models. 4,[16][17][18][19][20][21][22][23][24][25][26][27][30][31][32][33] Specifically, mixed model-based SKAT/SKATO/ SKAT-C performs well when (a) the number of causal variants is large and (b) each causal variant contributes a small amount to the traits, as the assumption of mixed models is satisfied under these circumstances. 7,21,34 In most cases, however, fixed models perform better since the causal variants of complex disorders can be common or rare or a combination of the two and some causal variants may have relatively large effects.…”

Section: Introductionmentioning

confidence: 99%

“…7,21,34 In most cases, however, fixed models perform better since the causal variants of complex disorders can be common or rare or a combination of the two and some causal variants may have relatively large effects. 10,[16][17][18][19][20][21][22][23][24][25][26][27] If the number of causal variants is large and each causal variant contributes a small amount to the traits, it would be hard to show association as the power of a test can be low. 35 One may want to note that SKAT and SKAT-O were shown to have higher power than burden tests, which is another main method to analyze rare variants.…”

Section: Introductionmentioning

confidence: 99%

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Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

Chiu¹,

Jung²,

Chen³

et al. 2016

Eur J Hum Genet

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To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, HotellingLawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. INTRODUCTION Meta-analysis of multiple studies and pleiotropy analysis of multiple traits are two areas in association studies that recently have received extensive attention in the literature. 1-10 To our knowledge, metaanalysis and pleiotropy analysis have been performed separately so far, and there are no gene-based meta-analysis methods for combining multiple studies together and for carrying out a unified pleiotropy analysis. Here, multivariate functional linear models (MFLM) are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates in a meta-analysis context. The goal is to take the advantage of both meta-analysis and pleiotropy analysis in order to improve power and to carry out a unified analysis of multiple studies and multiple quantitative traits of complex disorders.A noticeable feature of next-generation sequencing data is that dense panels of genetic variants are available via high-throughput sequencing technology, and so we face high-dimension genetic data. [11][12][13][14] The genetic data can consist of rare variants, or common variants, or a combination of the two, where the rare variants' minor allele frequencies (MAFs) are less than 0.01 ∼ 0.05. The high dimensionality of genetic data and the presence of dense rare variants raise

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“…neuroimaging endophenotypes (Shen et al, 2010;Zhang et al, 2014), can be used to boost power and illuminate on underlying biological mechanisms as compared to popular disease-based single trait analyses; see a review by Yang and Wang (2013). Most of the existing association tests for multiple traits are based on individual-level data (Basu et al, 2013;Fan et al, 2015Fan et al, , 2016Tang and Ferreira, 2012;Wang et al, 2015;Wang et al, 2016;Yang et al, 2010;Zhang et al, 2014) with only few exceptions such as MGAS (Van der Sluis et al, 2015) and metaCCA (Cichonska et al, 2016). Third, to increase the sample size, large consortia are being formed, aiming for meta analysis of multiple GWAS, for which often only summary statistics for single SNP-single trait associations, rather than individual-level genotypic and phenotypic data, are available.…”

Section: Introductionmentioning

confidence: 99%

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

Kwak

Pan

2016

Bioinformatics

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Summary: To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene-or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP-and traitlevels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a metaanalysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. Availability and Implementation: The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/.

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Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models

Cited by 18 publications

References 38 publications

Across‐Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression

Across‐Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

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