Bootstrat: Population Informed Bootstrapping for Rare Variant Tests

AbstractRecent advances in genotyping and sequencing technologies have made detecting rare variants in large cohorts possible. Various analytic methods for associating disease to rare variants have been proposed, including burden tests, C-alpha and SKAT. Most of these methods, however, assume that samples come from a homogeneous population, which is not realistic for analyses of large samples. Not correcting for population stratification causes inflated p-values and false-positive associations. Here we propose a population-informed bootstrap resampling method that controls for population stratification (Bootstrat) in rare variant tests. In essence, the Bootstrat procedure uses genetic distance to create a phenotype probability for each sample. We show that this empirical approach can effectively correct for population stratification while maintaining statistical power comparable to established methods of controlling for population stratification. The Bootstrat scheme can be easily applied to existing rare variant testing methods with reasonable computational complexity.Author SummaryRecent technology advances have enabled large-scale analysis of rare variants, but properly testing rare variants remains a significant challenge as most rare variant testing methods assume a sample of homogenous ethnicity, an assumption often not true for large cohorts. Failure to account for this heterogeneity increases the type I error rate. Here we propose a bootstrap scheme applicable to most existing rare variant testing methods to control for population heterogeneity. This scheme uses a randomization layer to establish a null distribution of the test statistics while preserving the sample genetic relationships. The null distribution is then used to calculate an empirical p-value that accounts for population heterogeneity. We demonstrate how this scheme successfully controls the type I error rate without loss of statistical power.

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Optimal selection of genetic variants for adjustment of population stratification in European association studies

Briefings in Bioinformatics ◽

10.1093/bib/bbz023 ◽

2019 ◽

Vol 21 (3) ◽

pp. 753-761 ◽

Cited By ~ 2

Author(s):

Regina Brinster ◽

Dominique Scherer ◽

Justo Lorenzo Bermejo

Keyword(s):

Genetic Variants ◽

Population Stratification ◽

Statistical Power ◽

Type I Error ◽

Association Studies ◽

Reference Sample ◽

Error Rates ◽

The Cancer Genome Atlas ◽

Type I ◽

Genotype Data

Abstract Population stratification is usually corrected relying on principal component analysis (PCA) of genome-wide genotype data, even in populations considered genetically homogeneous, such as Europeans. The need to genotype only a small number of genetic variants that show large differences in allele frequency among subpopulations—so-called ancestry-informative markers (AIMs)—instead of the whole genome for stratification adjustment could represent an advantage for replication studies and candidate gene/pathway studies. Here we compare the correction performance of classical and robust principal components (PCs) with the use of AIMs selected according to four different methods: the informativeness for assignment measure ($IN$-AIMs), the combination of PCA and F-statistics, PCA-correlated measurement and the PCA weighted loadings for each genetic variant. We used real genotype data from the Population Reference Sample and The Cancer Genome Atlas to simulate European genetic association studies and to quantify type I error rate and statistical power in different case–control settings. In studies with the same numbers of cases and controls per country and control-to-case ratios reflecting actual rates of disease prevalence, no adjustment for population stratification was required. The unnecessary inclusion of the country of origin, PCs or AIMs as covariates in the regression models translated into increasing type I error rates. In studies with cases and controls from separate countries, no investigated method was able to adequately correct for population stratification. The first classical and the first two robust PCs achieved the lowest (although inflated) type I error, followed at some distance by the first eight $IN$-AIMs.

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Advice on comparing two independent samples of circular data in biology

Scientific Reports ◽

10.1038/s41598-021-99299-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lukas Landler ◽

Graeme D. Ruxton ◽

E. Pascal Malkemper

Keyword(s):

Error Rate ◽

Statistical Power ◽

Type I Error ◽

Statistical Treatment ◽

Good Control ◽

Circular Data ◽

Type I ◽

Trigonometric Functions ◽

Biological Variables ◽

Two Samples

AbstractMany biological variables are recorded on a circular scale and therefore need different statistical treatment. A common question that is asked of such circular data involves comparison between two groups: Are the populations from which the two samples are drawn differently distributed around the circle? We compared 18 tests for such situations (by simulation) in terms of both abilities to control Type-I error rate near the nominal value, and statistical power. We found that only eight tests offered good control of Type-I error in all our simulated situations. Of these eight, we were able to identify the Watson’s U2 test and a MANOVA approach, based on trigonometric functions of the data, as offering the best power in the overwhelming majority of our test circumstances. There was often little to choose between these tests in terms of power, and no situation where either of the remaining six tests offered substantially better power than either of these. Hence, we recommend the routine use of either Watson’s U2 test or MANOVA approach when comparing two samples of circular data.

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Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

10.1101/552950 ◽

2019 ◽

Author(s):

Zilin Li ◽

Xihao Li ◽

Yaowu Liu ◽

Jincheng Shen ◽

Han Chen ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variant ◽

Type I Error ◽

Rare Variants ◽

Error Rates ◽

Type I ◽

Whole Genome ◽

Rare Variant Association ◽

Dynamic Scan

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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Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1040 ◽

2004 ◽

Vol 3 (1) ◽

pp. 1-69 ◽

Cited By ~ 53

Author(s):

Sandrine Dudoit ◽

Mark J. van der Laan ◽

Katherine S. Pollard

Keyword(s):

Error Rate ◽

Multiple Testing ◽

Type I Error ◽

Null Distribution ◽

Error Rates ◽

Single Step ◽

Type I ◽

Test Statistics ◽

Testing Procedures ◽

Multiple Testing Procedures

The present article proposes general single-step multiple testing procedures for controlling Type I error rates defined as arbitrary parameters of the distribution of the number of Type I errors, such as the generalized family-wise error rate. A key feature of our approach is the test statistics null distribution (rather than data generating null distribution) used to derive cut-offs (i.e., rejection regions) for these test statistics and the resulting adjusted p-values. For general null hypotheses, corresponding to submodels for the data generating distribution, we identify an asymptotic domination condition for a null distribution under which single-step common-quantile and common-cut-off procedures asymptotically control the Type I error rate, for arbitrary data generating distributions, without the need for conditions such as subset pivotality. Inspired by this general characterization of a null distribution, we then propose as an explicit null distribution the asymptotic distribution of the vector of null value shifted and scaled test statistics. In the special case of family-wise error rate (FWER) control, our method yields the single-step minP and maxT procedures, based on minima of unadjusted p-values and maxima of test statistics, respectively, with the important distinction in the choice of null distribution. Single-step procedures based on consistent estimators of the null distribution are shown to also provide asymptotic control of the Type I error rate. A general bootstrap algorithm is supplied to conveniently obtain consistent estimators of the null distribution. The special cases of t- and F-statistics are discussed in detail. The companion articles focus on step-down multiple testing procedures for control of the FWER (van der Laan et al., 2004b) and on augmentations of FWER-controlling methods to control error rates such as tail probabilities for the number of false positives and for the proportion of false positives among the rejected hypotheses (van der Laan et al., 2004a). The proposed bootstrap multiple testing procedures are evaluated by a simulation study and applied to genomic data in the fourth article of the series (Pollard et al., 2004).

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A more efficient three-arm non-inferiority test based on pooled estimators of the homogeneous variance

Statistical Methods in Medical Research ◽

10.1177/0962280216681036 ◽

2016 ◽

Vol 27 (8) ◽

pp. 2437-2446 ◽

Cited By ~ 1

Author(s):

Hezhi Lu ◽

Hua Jin ◽

Weixiong Zeng

Keyword(s):

Sample Size ◽

Error Rate ◽

Statistical Power ◽

Type I Error ◽

Statistical Testing ◽

New Method ◽

Type I ◽

Simulation Studies ◽

Testing Framework ◽

Better Than

Hida and Tango established a statistical testing framework for the three-arm non-inferiority trial including a placebo with a pre-specified non-inferiority margin to overcome the shortcomings of traditional two-arm non-inferiority trials (such as having to choose the non-inferiority margin). In this paper, we propose a new method that improves their approach with respect to two aspects. We construct our testing statistics based on the best unbiased pooled estimators of the homogeneous variance; and we use the principle of intersection-union tests to determine the rejection rule. We theoretically prove that our test is better than that of Hida and Tango for large sample sizes. Furthermore, when that sample size was small or moderate, our simulation studies showed that our approach performed better than Hida and Tango’s. Although both controlled the type I error rate, their test was more conservative and the statistical power of our test was higher.

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Likelihood-based Tests for Detecting Circadian Rhythmicity and Differential Circadian Patterns in Transcriptomic Applications

10.1101/2021.02.23.432538 ◽

2021 ◽

Author(s):

Haocheng Ding ◽

Lingsong Meng ◽

Andrew C. Liu ◽

Michelle L. Gumz ◽

Andrew J. Bryant ◽

...

Keyword(s):

Error Rate ◽

Statistical Power ◽

Type I Error ◽

Brain Aging ◽

Circadian Rhythmicity ◽

Postmortem Brain ◽

Superior Performance ◽

Type I ◽

Type I Error Rate ◽

Circadian Patterns

AbstractCircadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been founded to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level, and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR rhythmicity and LR diff was demonstrated in two real data applications, including a brain aging data (gene expression microarray data of human postmortem brain) and a time-restricted feeding data (RNA sequencing data of human skeletal muscles). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

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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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Controlling for human population stratification in rare variant association studies

Scientific Reports ◽

10.1038/s41598-021-98370-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Matthieu Bouaziz ◽

Jimmy Mullaert ◽

Benedetta Bigio ◽

Yoann Seeleuthner ◽

Jean-Laurent Casanova ◽

...

Keyword(s):

Population Stratification ◽

Type I Error ◽

Rare Variants ◽

Association Studies ◽

Genetic Association Studies ◽

Type I ◽

Sample Sizes ◽

Rare Disorders ◽

Type I Errors ◽

Large Numbers

AbstractPopulation stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.

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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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Design and Statistical Analysis of Pooled Next Generation Sequencing for Rare Variants

Journal of Probability and Statistics ◽

10.1155/2012/524724 ◽

2012 ◽

Vol 2012 ◽

pp. 1-19 ◽

Cited By ~ 3

Author(s):

Tao Wang ◽

Chang-Yun Lin ◽

Yuanhao Zhang ◽

Ruofeng Wen ◽

Kenny Ye

Keyword(s):

Next Generation Sequencing ◽

Error Rate ◽

Statistical Power ◽

Rare Variants ◽

Good Control ◽

Testing Procedure ◽

Sequencing Error ◽

Type I ◽

Next Generation ◽

Generation Sequencing

Next generation sequencing (NGS) is a revolutionary technology for biomedical research. One highly cost-efficient application of NGS is to detect disease association based on pooled DNA samples. However, several key issues need to be addressed for pooled NGS. One of them is the high sequencing error rate and its high variability across genomic positions and experiment runs, which, if not well considered in the experimental design and analysis, could lead to either inflated false positive rates or loss in statistical power. Another important issue is how to test association of a group of rare variants. To address the first issue, we proposed a new blocked pooling design in which multiple pools of DNA samples from cases and controls are sequenced together on same NGS functional units. To address the second issue, we proposed a testing procedure that does not require individual genotypes but by taking advantage of multiple DNA pools. Through a simulation study, we demonstrated that our approach provides a good control of the type I error rate, and yields satisfactory power compared to the test-based on individual genotypes. Our results also provide guidelines for designing an efficient pooled.

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