scholarly journals An evaluation of approaches for rare variant association analyses of binary traits in related samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ming-Huei Chen ◽  
Achilleas Pitsillides ◽  
Qiong Yang
Keyword(s):  
Logistic Regression ◽  
Rare Variants ◽  
Association Studies ◽  
Family Relationship ◽  
Genetic Association Studies ◽  
Error Rates ◽  
Ratio Test ◽  
Type I ◽  
Association Analyses ◽  
Binary Traits

AbstractRecognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Analyzing rare variants poses challenges for binary traits in that some genotype categories may have few or no observed events, causing bias and inflation in commonly used methods. Several methods have recently been proposed to better handle rare variants while accounting for family relationship, but their performances have not been thoroughly evaluated together. Here we compare several existing approaches including SAIGE but not limited to related samples using simulations based on the Framingham Heart Study samples and genotype data from Illumina HumanExome BeadChip where rare variants are the majority. We found that logistic regression with likelihood ratio test applied to related samples was the only approach that did not have inflated type I error rates in both single variant test (SVT) and gene-based tests, followed by Firth logistic regression that had inflation in its direction insensitive gene-based test at prevalence 0.01 only, applied to either related or unrelated samples, though theoretically logistic regression and Firth logistic regression do not account for relatedness in samples. SAIGE had inflation in SVT at prevalence 0.1 or lower and the inflation was eliminated with a minor allele count filter of 5. As for power, there was no approach that outperformed others consistently among all single variant tests and gene-based tests.

scholarly journals Trans-ethnic meta-analysis of rare variants in sequencing association studies

Biostatistics ◽  
2019 ◽  
Author(s):  
Jingchunzi Shi ◽  
Michael Boehnke ◽  
Seunggeun Lee
Keyword(s):  
Rare Variants ◽  
Sequence Data ◽  
Association Studies ◽  
Meta Analysis ◽  
Error Rates ◽  
Efficient Estimation ◽  
Gene Region ◽  
Type I ◽  
Test Statistic ◽  
Different Populations

Summary Trans-ethnic meta-analysis is a powerful tool for detecting novel loci in genetic association studies. However, in the presence of heterogeneity among different populations, existing gene-/region-based rare variants meta-analysis methods may be unsatisfactory because they do not consider genetic similarity or dissimilarity among different populations. In response, we propose a score test under the modified random effects model for gene-/region-based rare variants associations. We adapt the kernel regression framework to construct the model and incorporate genetic similarities across populations into modeling the heterogeneity structure of the genetic effect coefficients. We use a resampling-based copula method to approximate asymptotic distribution of the test statistic, enabling efficient estimation of p-values. Simulation studies show that our proposed method controls type I error rates and increases power over existing approaches in the presence of heterogeneity. We illustrate our method by analyzing T2D-GENES consortium exome sequence data to explore rare variant associations with several traits.

scholarly journals Controlling for human population stratification in rare variant association studies

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.

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

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.

scholarly journals Testing an Optimally Weighted Combination of Common and/or Rare Variants with Multiple Traits

2018 ◽  
Author(s):  
Zhenchuan Wang ◽  
Qiuying Sha ◽  
Kui Zhang ◽  
Shuanglin Zhang
Keyword(s):  
Statistical Power ◽  
Rare Variants ◽  
Association Studies ◽  
Error Rates ◽  
Common Variant ◽  
Joint Analysis ◽  
Type I ◽  
Multiple Traits ◽  
Multiple Sequence ◽  
Weighted Combination

AbstractJoint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single common variant. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

scholarly journals Bayestrat: Population Stratification Correction Using Bayesian Shrinkage Prior for Genetic Association Studies

2021 ◽  
Author(s):  
Zilu Liu ◽  
Asuman Turkmen ◽  
Shili Lin
Keyword(s):  
Genetic Association ◽  
Population Stratification ◽  
Linear Mixed Model ◽  
Type I Error ◽  
Association Studies ◽  
Random Effect ◽  
Genetic Association Studies ◽  
Error Rates ◽  
Type I ◽  
Bayesian Shrinkage

In genetic association studies with common diseases, population stratification is a major source of confounding. Principle component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for population stratification. Previous studies have shown that LMM can be interpreted as including all principle components (PCs) as random-effect covariates. However, including all PCs in LMM may inflate type I error in some scenarios due to redundancy, while including only a few pre-selected PCs in PCR may fail to fully capture the genetic diversity. Here, we propose a statistical method under the Bayesian framework, Bayestrat, that utilizes appropriate shrinkage priors to shrink the effects of non- or minimally confounded PCs and improve the identification of highly confounded ones. Simulation results show that Bayestrat consistently achieves lower type I error rates yet higher power, especially when the number of PCs included in the model is large. We also apply our method to two real datasets, the Dallas Heart Studies (DHS) and the Multi-Ethnic Study of Atherosclerosis (MESA), and demonstrate the superiority of Bayestrat over commonly used methods.

scholarly journals Testing gene-environment interactions for rare and/or common variants in sequencing association studies

2019 ◽  
Author(s):  
Zihan Zhao ◽  
Jianjun Zhang ◽  
Qiuying Sha ◽  
Han Hao
Keyword(s):  
Rare Variants ◽  
Association Studies ◽  
Error Rates ◽  
Type I ◽  
Common Variants ◽  
Next Generation Sequencing Technology ◽  
Gene Environment ◽  
Variable Weight ◽  
Protective Variants ◽  
Weighted Combination

AbstractThe risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on testing the main effects of common and/or rare genetic variants. There are limited methods developed to test the effects of GE interactions for rare variants only or rare and common variants simultaneously. In this study, we develop novel approaches to test the effects of GE interactions of rare and/or common risk, and/or protective variants in sequencing association studies. We propose two approaches: 1) testing the effects of an optimally weighted combination of GE interactions for rare variants (TOW-GE); 2) testing the effects of a weighted combination of GE interactions for both rare and common variants (variable weight TOW-GE, VW-TOW-GE). Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are GE interactions’ effects for rare risk and/or protective variants; VW-TOW-GE is more powerful when there are GE interactions’ effects for both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to the directions of effects of causal GE interactions. We demonstrate the applications of TOW-GE and VW-TOW-GE using an imputed data from the COPDGene Study.

scholarly journals Controlling for Human Population Stratification in Rare Variant Association Studies

2020 ◽  
Author(s):  
Matthieu Bouaziz ◽  
Jimmy Mullaert ◽  
Benedetta Bigio ◽  
Yoann Seeleuthner ◽  
Jean-Laurent Casanova ◽  
...  
Keyword(s):  
Principal Components ◽  
Population Stratification ◽  
Type I Error ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Type I ◽  
Sample Sizes ◽  
Type I Errors ◽  
Ngs Data

AbstractPopulation stratification is a strong confounding factor in human genetic association studies. In analyses of rare variants, the main correction strategies based on principal components (PC) and linear mixed models (LMM), may yield conflicting conclusions, due to both the specific type of structure induced by rare variants and the particular statistical features of association tests. Studies evaluating these approaches generally focused on specific situations with limited types of simulated structure and large sample sizes. We investigated the properties of several correction methods in the context of a large simulation study using real exome data, and several within- and between- continent stratification scenarios. We also considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. In this context, we focused on a genetic model with a phenotype driven by rare deleterious variants well suited for a burden test. For analyses of large samples, we found that accounting for stratification was more difficult with a continental structure than with a worldwide structure. LMM failed to maintain a correct type I error in many scenarios, whereas PCs based on common variants failed only in the presence of extreme continental stratification. When a sample of 50 cases was considered, an inflation of type I errors was observed with PC for small numbers of controls (≤100), and with LMM for large numbers of controls (≥1000). We also tested a promising novel adapted local permutation method (LocPerm), which maintained a correct type I error in all situations. All approaches capable of correcting for stratification properly had similar powers for detecting actual associations pointing out that the key issue is to properly control type I errors. Finally, we found that adding a large panel of external controls (e.g. extracted from publicly available databases) was an efficient way to increase the power of analyses including small numbers of cases, provided an appropriate stratification correction was used.Author SummaryGenetic association studies focusing on rare variants using next generation sequencing (NGS) data have become a common strategy to overcome the shortcomings of classical genome-wide association studies for the analysis of rare and common diseases. The issue of population stratification remains however a substantial question that has not been fully resolved when analyzing NGS data. In this work, we propose a comprehensive evaluation of the main strategies to account for stratification, that are principal components and linear mixed model, along with a novel approach based on local permutations (LocPerm). We compared these correction methods in many different settings, considering several types of population structures, sample sizes or types of variants. Our results highlighted important limitations of some classical methods as those using principal components (in particular in small samples) and linear mixed models (in several situations). In contrast, LocPerm maintained a correct type I error in all situations. Also, we showed that adding a large panel of external controls, e.g coming from publicly available databases, is an efficient strategy to increase the power of an analysis including a low number of cases, as long as an appropriate stratification correction is used. Our findings provide helpful guidelines for many researchers working on rare variant association studies.

scholarly journals Comparison of haplotype-based tests for detecting gene–environment interactions with rare variants

2019 ◽  
Vol 21 (3) ◽  
pp. 851-862 ◽  
Author(s):  
Charalampos Papachristou ◽  
Swati Biswas
Keyword(s):  
Lung Cancer ◽  
Rare Variants ◽  
Practical Interest ◽  
Serum Triglyceride ◽  
Error Rates ◽  
Type I ◽  
Rare Haplotype ◽  
Data Set ◽  
Cancer Data ◽  
Gene Environment

Abstract Dissecting the genetic mechanism underlying a complex disease hinges on discovering gene–environment interactions (GXE). However, detecting GXE is a challenging problem especially when the genetic variants under study are rare. Haplotype-based tests have several advantages over the so-called collapsing tests for detecting rare variants as highlighted in recent literature. Thus, it is of practical interest to compare haplotype-based tests for detecting GXE including the recent ones developed specifically for rare haplotypes. We compare the following methods: haplo.glm, hapassoc, HapReg, Bayesian hierarchical generalized linear model (BhGLM) and logistic Bayesian LASSO (LBL). We simulate data under different types of association scenarios and levels of gene–environment dependence. We find that when the type I error rates are controlled to be the same for all methods, LBL is the most powerful method for detecting GXE. We applied the methods to a lung cancer data set, in particular, in region 15q25.1 as it has been suggested in the literature that it interacts with smoking to affect the lung cancer susceptibility and that it is associated with smoking behavior. LBL and BhGLM were able to detect a rare haplotype–smoking interaction in this region. We also analyzed the sequence data from the Dallas Heart Study, a population-based multi-ethnic study. Specifically, we considered haplotype blocks in the gene ANGPTL4 for association with trait serum triglyceride and used ethnicity as a covariate. Only LBL found interactions of haplotypes with race (Hispanic). Thus, in general, LBL seems to be the best method for detecting GXE among the ones we studied here. Nonetheless, it requires the most computation time.

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

2019 ◽  
Vol 21 (3) ◽  
pp. 753-761 ◽  
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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