BETASEQ: a powerful novel method to control type-I error inflation in partially sequenced data for rare variant association testing

AbstractWhole genome sequencing (WGS) studies are being widely conducted to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set based analyses are commonly used to analyze rare variants. However, existing variant-set based approaches need to pre-specify genetic regions for analysis, and hence are not directly applicable to WGS data due to the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding window method requires pre-specifying fixed window sizes, which are often unknown as a priori, are difficult to specify in practice and are subject to limitations given genetic association region sizes are likely to vary across the genome and phenotypes. We propose a computationally-efficient and dynamic scan statistic method (Scan the Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the locations of rare-variants association regions without the need of specifying a prior fixed window size. The proposed method controls the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected rare variants association region sizes to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative rare-variant association detection methods while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

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Taking population stratification into account by local permutations in rare-variant association studies on small samples

10.1101/2020.01.29.924977 ◽

2020 ◽

Cited By ~ 1

Author(s):

J. Mullaert ◽

M. Bouaziz ◽

Y. Seeleuthner ◽

B. Bigio ◽

J-L. Casanova ◽

...

Keyword(s):

Sample Size ◽

Rare Variant ◽

Population Stratification ◽

Type I Error ◽

Small Sample Size ◽

Association Studies ◽

Small Sample ◽

Small Samples ◽

Type I ◽

Rare Variant Association

AbstractMany methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individuals are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.

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A permutation method for detecting trend correlations in rare variant association studies

Genetics Research ◽

10.1017/s0016672319000120 ◽

2019 ◽

Vol 101 ◽

Author(s):

Lifeng Liu ◽

Pengfei Wang ◽

Jingbo Meng ◽

Lili Chen ◽

Wensheng Zhu ◽

...

Keyword(s):

Rare Variant ◽

Type I Error ◽

Rare Variants ◽

Association Studies ◽

Complex Diseases ◽

Type I ◽

Phenotypic Variance ◽

Rare Variant Association ◽

Significance Level ◽

Association Analyses

Abstract In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

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Faculty Opinions recommendation of A statistical approach for rare-variant association testing in affected sibships.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725400621.793510953 ◽

2015 ◽

Author(s):

Ellen Wijsman

Keyword(s):

Rare Variant ◽

Statistical Approach ◽

Rare Variant Association ◽

Association Testing

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REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

Advances in Data Analysis and Classification ◽

10.1007/s11634-021-00441-y ◽

2021 ◽

Author(s):

Zaheer Ahmed ◽

Alberto Cassese ◽

Gerard van Breukelen ◽

Jan Schepers

Keyword(s):

Null Hypothesis ◽

Type I Error ◽

Type I ◽

The Novel ◽

Simulation Studies ◽

Test Statistic ◽

Clustering Model ◽

Novel Method ◽

Main Effects ◽

Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

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A Statistical Approach for Rare-Variant Association Testing in Affected Sibships

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2015.01.020 ◽

2015 ◽

Vol 96 (4) ◽

pp. 543-554 ◽

Cited By ~ 14

Author(s):

Michael P. Epstein ◽

Richard Duncan ◽

Erin B. Ware ◽

Min A. Jhun ◽

Lawrence F. Bielak ◽

...

Keyword(s):

Rare Variant ◽

Statistical Approach ◽

Rare Variant Association ◽

Association Testing

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Rare variant association testing in the non-coding genome

Human Genetics ◽

10.1007/s00439-020-02190-y ◽

2020 ◽

Vol 139 (11) ◽

pp. 1345-1362

Author(s):

Ozvan Bocher ◽

Emmanuelle Génin

Keyword(s):

Rare Variant ◽

Rare Variant Association ◽

Association Testing

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Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2011.05.029 ◽

2011 ◽

Vol 89 (1) ◽

pp. 82-93 ◽

Cited By ~ 1381

Author(s):

Michael C. Wu ◽

Seunggeun Lee ◽

Tianxi Cai ◽

Yun Li ◽

Michael Boehnke ◽

...

Keyword(s):

Rare Variant ◽

Association Test ◽

Sequence Kernel Association Test ◽

Sequencing Data ◽

Rare Variant Association ◽

Association Testing

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MARS: leveraging allelic heterogeneity to increase power of association testing

Genome Biology ◽

10.1186/s13059-021-02353-8 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 1

Author(s):

Farhad Hormozdiari ◽

Junghyun Jung ◽

Eleazar Eskin ◽

Jong Wha J. Joo

Keyword(s):

Type I Error ◽

Association Studies ◽

Simulated Data ◽

Real Data ◽

Association Test ◽

Type I ◽

Genome Wide Association Studies ◽

Association Testing ◽

Causal Status ◽

Causal Variants

AbstractIn standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

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Effective QTL Discovery Incorporating Genomic Annotations

10.1101/032003 ◽

2015 ◽

Cited By ~ 1

Author(s):

Xiaoquan Wen

Keyword(s):

Type I Error ◽

Hierarchical Modeling ◽

Enrichment Analysis ◽

Control Procedure ◽

Type I ◽

Transcription Start Sites ◽

Cell Functions ◽

Level Statistics ◽

Novel Method ◽

Qtl Discovery

Mapping molecular QTLs has emerged as an important tool for understanding the genetic basis of cell functions. With the increasing availability of functional genomic data, it is natural to incorporate genomic annotations into QTL discovery. In this paper, we describe a novel method, named TORUS, for integrative QTL discovery. Using hierarchical modeling, our approach embeds a rigorous enrichment analysis to quantify the enrichment level of each annotation in target QTLs. This enrichment information is then used to identify QTLs by up-weighting the genetic variants with relevant annotations using a Bayesian false discovery rate control procedure. Our proposed method only requires summary-level statistics and is highly efficient computationally: it runs a few hundreds times faster than the current gold-standard QTL discovery approach that relies on permutations. Through simulation studies, we demonstrate that the proposed method performs accurate enrichment analysis and controls the desired type I error rate while greatly improving the power of QTL discovery when incorporating informative annotations. Finally, we analyze the recently released expression-genotype data from 44 human tissues generated by the GTEx project. By integrating the simple annotation of SNP distance to transcription start sites, we discover more genes that harbor expression-associated SNPs in all 44 tissues, with an average increase of 1,485 genes.

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