FunSPU: a versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data

Ma, Yiding; Wei, Peng

doi:10.1101/350355

Cited by 10 publications

(13 citation statements)

References 35 publications

(21 reference statements)

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“…First, as transcriptome-wide association studies (TWAS) ( Gamazon et al, 2015 ; Gusev et al, 2016 ) that incorporate eQTL-derived weights into a weighted Sum test ( Xu et al, 2017 ) to both improve statistical power and enhance biological interpretation, our proposed method can incorporate eQTL-derived weights into the test statistics of iSPU( γ ) and aiSPU. Also, some other functional weights ( He et al, 2017 ; Ma and Wei, 2019 ) can be equally applied. We expect that integrating functional genomic information will improve power and gain insights into the mechanisms of complex traits.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Test on High-dimensional Parameters in Generalized Linear Models

Pan³

2019

STAT SINICA

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Significance testing for high-dimensional generalized linear models (GLMs) has been increasingly needed in various applications, however, existing methods are mainly based on a sum of squares of the score vector and only powerful under certain alternative hypotheses. In practice, depending on whether the true association pattern under an alternative hypothesis is sparse or dense or between, the existing tests may or may not be powerful. In this paper, we propose an adaptive test on a high-dimensional parameter of a GLM (in the presence of a low-dimensional nuisance parameter), which can maintain high power across a wide range of scenarios. To evaluate its p-value, its asymptotic null distribution is derived. We conduct simulations to demonstrate the superior performance of the proposed test. In addition, we apply it and other existing tests to an Alzheimer's Disease Neuroimaging Initiative (ADNI) * Correspondence: wuxx0845@umn.edu (C.W.), weip@biostat.umn.edu (W.P.) † Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

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Section: Discussionmentioning

confidence: 99%

An Adaptive Test on High-dimensional Parameters in Generalized Linear Models

Pan³

2019

STAT SINICA

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“…In our data example, gene B4GALNT2 was discovered by the aSPU-meta test for the AA subjects in the presence of high between-study heterogeneity and a large number of likely neutral RVs; in contrast, it was missed by competing methods. Although we did not focus on the use of variantspecific or study-specific weights, appropriate incorporation of such weights may increase the statistical power and interpretation, for example, using the functional weights (He, Xu, Lee, & Ionita-Laza, 2017;Ma & Wei, 2019;Zhan & Liu, 2015). We also note that, similar to other competing tests for RVs, one potential drawback of the current implementation of the aSPU-meta test using Monte-Carlo simulations is its use of the asymptotic normal distribution of the score vector, which may not hold for extremely unbalanced case-control studies, and other resampling methods like permutation-based ones may need to be adopted for more accurate p value calculations (Dey et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

An adaptive test for meta‐analysis of rare variant association studies

Yang

Kim

et al. 2019

Genetic Epidemiology

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Single genome-wide studies may be underpowered to detect trait-associated rare variants with moderate or weak effect sizes. As a viable alternative, meta-analysis is widely used to increase power by combining different studies. The power of meta-analysis critically depends on the underlying association patterns and heterogeneity levels, which are unknown and vary from locus to locus. However, existing methods mainly focus on one or only a few combinations of the association pattern and heterogeneity level, thus may lose power in many situations. To address this issue, we propose a general and unified framework by combining a class of tests including and beyond some existing ones, leading to high power across a wide range of scenarios. We demonstrate that the proposed test is more powerful than some existing methods in simulation studies, then show their performance with the NHLBI Exome-Sequencing Project (ESP) data.One gene (B4GALNT2) was found by our proposed test, but not by others, to be statistically significantly associated with plasma triglyceride. The signal was driven by African-ancestry subjects but it was previously reported to be associated with coronary artery disease among European-ancestry subjects. We implemented our method in an R package aSPUmeta, publicly available at https://github. com/ytzhong/metaRV and will be on CRAN soon. K E Y W O R D SaSPU, genetic heterogeneity, statistical power, whole-exome sequencing, whole-genome sequencing *Tianzhong Yang and Junghi Kim contributed equally to this work.

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“…The inner sum of the weighted score function with power

γ_{1}

can be regarded as the aGE test statistic for weighted genotype in each tissue, and they are normalized to the power of

1 ∕ γ_{1}

before taking a second power

γ_{2}

. Based on our previous investigations (Kwak & Pan, 2016; Ma & Wei, 2019; Pan et al, 2014) and simulation study to be detailed later, we would recommend using

Γ_{1} = {1, 2, 3, 4, 5, 6}

and

Γ_{2} = {1, 2, 4}

, which suffice to strike a balance between statistical power and computational complexity. When

γ_{1} = 1

, the GEw

(γ_{1} = 1, \cdot)

test is equivalent to the adaptive weighted burden test, assuming that the interaction effects of SNPs are proportional to the external weights, that is the full model for the m th tissue is reduced to

h (μ_{i}) = X_{i} β_{X} + β_{e} E_{i} + \sum_{j}^{q} β_{j} G_{i j} + α^{(m)} \sum_{j}^{q} w_{j}^{(m)} S_{i j} .

Note that formulation () avoids the potential problem of an inflated Type 1 error rate in model ().…”

Section: Methodsmentioning

confidence: 99%

Incorporating multiple sets of eQTL weights into gene‐by‐environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

Yang

Tang

Risch

et al. 2020

Genetic Epidemiology

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It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene‐by‐environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data‐adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case–Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome‐wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking‐related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.

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FunSPU: a versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data

Cited by 10 publications

References 35 publications

An Adaptive Test on High-dimensional Parameters in Generalized Linear Models

An Adaptive Test on High-dimensional Parameters in Generalized Linear Models

An adaptive test for meta‐analysis of rare variant association studies

Incorporating multiple sets of eQTL weights into gene‐by‐environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

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