Kernel-Based Association Test

Yang, Hsin-Chou; Hsieh, He-Yen; Fann, Cathy S.J.

doi:10.1534/genetics.107.084616

Cited by 25 publications

(25 citation statements)

References 54 publications

(74 reference statements)

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“…Alternative approaches include hidden Markov models, the fused lasso, local regression and kernel-based approaches [50]–[54]. Nor is it clear that pooling -values is the best approach; a more powerful approach may be to pool test statistics instead of -values to account for the direction of the association.…”

Section: Discussionmentioning

confidence: 99%

Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

et al. 2012

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Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.

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Section: Discussionmentioning

confidence: 99%

Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

et al. 2012

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“…Previously, Yang et al. [] found a significant association between ALDX1 and two SNP markers (rs0724481 at 123.3 cM and rs0343819 at 132.11 cM) on chromosome 1. However, only the main gene effects for association study of ALDX1 were considered in their study.…”

Section: Resultsmentioning

confidence: 99%

SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

Fang

Chiu

2012

Genetic Epidemiology

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Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.

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“…There are multiple types of p -value combination methods. This paper considers a truncated product p -value statistic because of its good performance in our previous simulation study [12,13]. However, in the analysis of RA data, we also calculated empirical p -values of different combination methods including the minimum p -value statistic and Fisher's product p -value statistic for the top significant genes.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide gene-based association study

Yang

Liang

Chung³

et al. 2009

BMC Proc

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Genome-wide association studies, which analyzes hundreds of thousands of single-nucleotide polymorphisms to identify disease susceptibility genes, are challenging because the work involves intensive computation and complex modeling. We propose a two-stage genome-wide association scanning procedure, consisting of a single-locus association scan for the first stage and a gene-based association scan for the second stage. Marginal effects of single-nucleotide polymorphisms are examined by using the exact Armitage trend test or logistic regression, and gene effects are examined by using a p-value combination method. Compared with some existing single-locus and multilocus methods, the proposed method has the following merits: 1) convenient for definition of biologically meaningful regions, 2) powerful for detection of minor-effect genes, 3) helpful for alleviation of a multiple-testing problem, and 4) convenient for result interpretation. The method was applied to study Genetic Analysis Workshop 16 Problem 1 rheumatoid arthritis data, and strong association signals were found. The results show that the human major histocompatibility complex region is the most important genomic region associated with rheumatoid arthritis. Moreover, previously reported genes including PTPN22, C5, and IL2RB were confirmed; novel genes including HLA-DRA, BTNL2, C6orf10, NOTCH4, TAP2, and TNXB were identified by our analysis.

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Kernel-Based Association Test

Cited by 25 publications

References 54 publications

Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

Genome-wide gene-based association study

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