A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies

Sha, Qiuying; Zhang, Kui; Zhang, Shuanglin

doi:10.1038/srep37444

Cited by 11 publications

(12 citation statements)

References 39 publications

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“…However, logistic regression may not be the most powerful analysis strategy for rare variants and so higher inflation might be observed with statistical approaches designed for rare variants. Since our model simulations included only a single candidate SNP, we could not evaluate any rare variant methods, such as SKAT (Wu et al, 2011), which can be modified to correct for ancestry (Luo et al, 2018), or evaluate any ancestry correction methods designed for rare variants, such as Sha et al (2016). Given the slight false positive rate inflation that we saw even after including PCs in the logistic regression model (Balding-Nichols simulation), a more in-depth exploration of PC-based corrections under EPS when there are rare variants is needed.…”

Section: Discussionmentioning

confidence: 99%

A Cautionary Note on the Effects of Population Stratification Under an Extreme Phenotype Sampling Design

Panarella

Burkett

2019

Front. Genet.

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Extreme phenotype sampling (EPS) is a popular study design used to reduce genotyping or sequencing costs. Assuming continuous phenotype data are available on a large cohort, EPS involves genotyping or sequencing only those individuals with extreme phenotypic values. Although this design has been shown to have high power to detect genetic effects even at smaller sample sizes, little attention has been paid to the effects of confounding variables, and in particular population stratification. Using extensive simulations, we demonstrate that the false positive rate under the EPS design is greatly inflated relative to a random sample of equal size or a “case-control”-like design where the cases are from one phenotypic extreme and the controls randomly sampled. The inflated false positive rate is observed even with allele frequency and phenotype mean differences taken from European population data. We show that the effects of confounding are not reduced by increasing the sample size. We also show that including the top principal components in a logistic regression model is sufficient for controlling the type 1 error rate using data simulated with a population genetics model and using 1,000 Genomes genotype data. Our results suggest that when an EPS study is conducted, it is crucial to adjust for all confounding variables. For genetic association studies this requires genotyping a sufficient number of markers to allow for ancestry estimation. Unfortunately, this could increase the costs of a study if sequencing or genotyping was only planned for candidate genes or pathways; the available genetic data would not be suitable for ancestry correction as many of the variants could have a true association with the trait.

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Section: Discussionmentioning

confidence: 99%

A Cautionary Note on the Effects of Population Stratification Under an Extreme Phenotype Sampling Design

Panarella

Burkett

2019

Front. Genet.

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“…Sha et al. () show the effectiveness of PC‐nonp in controlling for population substructure with burden RV tests, and it is of great interest to evaluate the performance of PC‐nonp with SKAT. However, in our preliminary explorations, it seems that SKAT‐PC‐nonp was not able to control for confounding regardless which type of variants are used to obtain PCs (data not shown).…”

Section: Discussionmentioning

confidence: 99%

“…Unlike the typical PC-based correction methods that assume the top PCs have a linear effect on the traits, PC-nonp uses a nonparametric regression to model the potential nonlinear or complex effects of the PCs. Sha et al (2016) show the effectiveness of PC-nonp in controlling for population substructure with burden RV tests, and it is of great interest to evaluate the performance of PC-nonp with (from top to bottom) no confounders, confounders with a discrete spatial distribution, and confounders with a continuous spatial distribution. Different columns indicate the types of variants used to construct PC/VC, i.e., RV for rare variants, LFV for less frequent variants, CV for common variants, and AV for all variants SKAT.…”

Section: Discussionmentioning

confidence: 99%

“…In Simulation I with substructure confounders, we observe that SKAT‐PC does not always provide effective control of confounding effects. Recently, Sha, Zhang, and Zhang () proposes a new PC‐based method, called PC‐nonp, to control for population substructure. Unlike the typical PC‐based correction methods that assume the top PCs have a linear effect on the traits, PC‐nonp uses a nonparametric regression to model the potential nonlinear or complex effects of the PCs.…”

Section: Discussionmentioning

confidence: 99%

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On the substructure controls in rare variant analysis: Principal components or variance components?

Luo

Maity

et al. 2017

Genetic Epidemiology

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Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e.g., common, less frequent, rare, or all variants) should be used to effectively control for confounding effects. We found that (i) PC-based methods can account for confounding effects in most scenarios except for admixture, although the number of sufficient PCs depends on the PS complexity and the type of variants used. (ii) PCs based on all variants (i.e., common + less frequent + rare) tend to require equal or fewer sufficient PCs and often achieve higher power than PCs based on other variant types. (iii) VC-based methods can effectively adjust for confounding in all scenarios (even for admixture), though the type of variants should be used to construct VC may vary. (iv) VC based on all variants works consistently in all scenarios, though its power may be sometimes lower than VC based on other variant types. Given that the best-performed method and which variants to use depend on the underlying unknown confounding mechanisms, a robust strategy is to perform SKAT analyses using VC-based methods based on all variants.

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“…All these burden tests assume all the variants share the same effect direction and magnitude (after incorporating weights). Thus, any violation of this assumption can result in a loss of power 8 , 10 , 35 . To overcome the limitations of the burden tests, the data-adaptive sum test (aSum) was proposed 36 .…”

Section: Methodsmentioning

confidence: 99%

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Cao

Zhi

Huang

et al. 2017

Sci Rep

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Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

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A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies

Cited by 11 publications

References 39 publications

A Cautionary Note on the Effects of Population Stratification Under an Extreme Phenotype Sampling Design

A Cautionary Note on the Effects of Population Stratification Under an Extreme Phenotype Sampling Design

On the substructure controls in rare variant analysis: Principal components or variance components?

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

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