Identifying influential regions in extremely rare variants using a fixed-bin approach

Agne, Michael; Huang, Chien‐Hsun; Hu, Inchi; Wang, Maggie Haitian; Tian, Zheng; Lo, Shaw-Hwa

doi:10.1186/1753-6561-5-s9-s3

Cited by 3 publications

(23 citation statements)

References 8 publications

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“…Both Agne et al [2011] and Xu and George [2011] analyze the simulated affection status, and both methods are based on the number of rare variants in predefined genomic regions. Although both methods can accommodate different definitions of rare variants, Xu and George [2011] define a rare variant as one having a minor allele frequency (MAF) < 0.01 and Agne et al [2011] count only private variants, which occur once in the set of 697 unrelated individuals.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…They use the statistic’s normal approximation to obtain the p -values. For each fixed bin, Agne et al [2011] compare the number of private variants in case subjects to the number of SNPs in both case subjects and control subjects. The statistical significance of this discrepancy is then evaluated by permuting the individual affection status a large number of times.…”

Section: Methods and Resultsmentioning

confidence: 99%

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Detecting multiple causal rare variants in exome sequence data

Engelman

2011

Genetic Epidemiology

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Recent advances in sequencing technology have presented both opportunities and challenges, with limited statistical power to detect a single causal rare variant with practical sample sizes. To overcome this, the contributors to Group 1 of Genetic Analysis Workshop 17 sought to develop methods to detect the combined signal of multiple causal rare variants in a biologically meaningful way. The contributors used genes, genome location proximity, or genetic pathways as the basic unit in combining the information from multiple variants. Weaknesses of the exome sequence data and the relative strengths and weaknesses of the five approaches are discussed.

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Section: Methods and Resultsmentioning

confidence: 99%

Section: Methods and Resultsmentioning

confidence: 99%

Detecting multiple causal rare variants in exome sequence data

Engelman

2011

Genetic Epidemiology

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“…Out of 8,348,674 SNVs, 1,391,764 (17 %) were unused because they are not in or near any gene. We could group these SNVs by physical location and also incorporate them into the analysis [4]. We found limited association evidence of single-nucleotide polymorphisms (SNPs) identified from previous GWAS, possibly because of differences in study samples (ie, whites vs. Mexican Americans).…”

Section: Discussionmentioning

confidence: 99%

“…The dramatic increase in numbers of single nucleotide variants (SNVs) also raises computational and statistical challenges (eg, multiple testing issue). One practical strategy is to group multiple SNVs according to known functional information (eg, variants in a gene or a pathway) or location (eg, variants in a fix-sized bin [4]), and jointly analyze these SNVs [5, 6]. By grouping and testing multiple SNVs, we are able to aggregate association signals and reduce the number of tests.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide joint analysis of single-nucleotide variant sets and gene expression for hypertension and related phenotypes

Tong

Wei

2016

BMC Proc

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BackgroundWith the advance of next-generation sequencing technologies, the study of rare variants in targeted genome regions or even the whole genome becomes feasible. Nevertheless, the massive amount of sequencing data brings great computational and statistical challenges for association analyses. Aside from sequencing variants, other high-throughput omic data (eg, gene expression data) also become available, and can be incorporated into association analysis for better modeling and power improvement. This motivates the need of developing computationally efficient and powerful approaches to model the joint associations of multilevel omic data with complex human diseases.MethodsA similarity-based weighted U approach is used to model the joint effect of sequencing variants and gene expression. Using a Mexican American sample provided by Genetic Analysis Workshop 19 (GAW19), we performed a whole-genome joint association analysis of sequencing variants and gene expression with systolic (SBP) and diastolic blood pressure (DBP) and hypertension (HTN) phenotypes.ResultsThe whole-genome joint association analysis was completed in 80 min on a high-performance personal computer with an i7 4700 CPU and 8 GB memory. Although no gene reached statistical significance after adjusting for multiple testing, some top-ranked genes attained a high significance level and may have biological plausibility to hypertension-related phenotypes.ConclusionsThe weighted U approach is computationally efficient for high-dimensional data analysis, and is capable of integrating multiple levels of omic data into association analysis. Through a real data application, we demonstrate the potential benefit of using the new approach for joint association analysis of sequencing variants and gene expression.

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“…In the past, methods of studying rare variants have been applied that deal only with a marginal genetic effect [1]. It would seem logical to attempt to incorporate some sort of interaction into the analysis of this study.…”

Section: Introductionmentioning

confidence: 99%

Considering interactive effects in the identification of influential regions with extremely rare variants via fixed bin approach

Agne

Huang

et al. 2014

BMC Proc

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In this study, we analyze the Genetic Analysis Workshop 18 (GAW18) data to identify regions of single-nucleotide polymorphisms (SNPs), which significantly influence hypertension status among individuals. We have studied the marginal impact of these regions on disease status in the past, but we extend the method to deal with environmental factors present in data collected over several exam periods. We consider the respective interactions between such traits as smoking status and age with the genetic information and hope to augment those genetic regions deemed influential marginally with those that contribute via an interactive effect. In particular, we focus only on rare variants and apply a procedure to combine signal among rare variants in a number of "fixed bins" along the chromosome. We extend the procedure in Agne et al [1] to incorporate environmental factors by dichotomizing subjects via traits such as smoking status and age, running the marginal procedure among each respective category (i.e., smokers or nonsmokers), and then combining their scores into a score for interaction. To avoid overlap of subjects, we examine each exam period individually. Out of a possible 629 fixed-bin regions in chromosome 3, we observe that 11 show up in multiple exam periods for gene-smoking score. Fifteen regions exhibit significance for multiple exam periods for gene-age score, with 4 regions deemed significant for all 3 exam periods. The procedure pinpoints SNPs in 8 "answer" genes, with 5 of these showing up as significant in multiple testing schemes (Gene-Smoking, Gene-Age for Exams 1, 2, and 3).

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Identifying influential regions in extremely rare variants using a fixed-bin approach

Cited by 3 publications

References 8 publications

Detecting multiple causal rare variants in exome sequence data

Detecting multiple causal rare variants in exome sequence data

Genome-wide joint analysis of single-nucleotide variant sets and gene expression for hypertension and related phenotypes

Considering interactive effects in the identification of influential regions with extremely rare variants via fixed bin approach

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