Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

Yang, Tianzhong; Wu, Chong; Wei, Peng; Pan, Wei

doi:10.1093/hmg/ddz314

“…Our simulation study confirmed that correctly using 0‐1 weights improved power as compared to the unweighted test. Such a finding has also been reported in other functional studies (Li et al, 2013; Su, Di, & Hsu, 2017; C. Wu & Pan, 2018; Yang, Wu, Wei, & Pan, 2020).…”

Section: Discussionsupporting

confidence: 87%

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

Yang

¹

,

Tang

²

,

Risch

³

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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“…Here e A , e X , e Y are independent random error terms. In model (20), we allow a general relationship between A and X, and between A and Y. When the true causal direction is from Y to X, we have a corresponding true model.…”

Section: Further Extensionsmentioning

confidence: 99%

“…Second, since MR Steiger's method is based on using only a single SNP as IV, to improve both its statistical power and applicability, we extend it to allow multiple, and possibly correlated, SNPs. In particular, there is increasing interest in applying transcriptome-wide association studies (TWAS, or PrediXcan) to identify causal genes or other molecular/imaging endophenotypes by integrating GWAS with eQTL/ xQTL data [11][12][13][14][15][16][17][18][19][20]. In these applications, multiple correlated cis-SNPs near a gene are used as IVs to impute or predict the gene's expression level (or another endophenotype) to infer whether the gene's expression has a causal effect on a trait, say coronary artery disease (CAD); it is assumed that the causal relationship (if existing) is from the gene to the trait.…”

Section: Introductionmentioning

confidence: 99%

Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data

Xue

¹

,

Pan

²

2020

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Orienting the causal relationship between pairs of traits is a fundamental task in scientific research with significant implications in practice, such as in prioritizing molecular targets and modifiable risk factors for developing therapeutic and interventional strategies for complex diseases. A recent method, called Steiger’s method, using a single SNP as an instrument variable (IV) in the framework of Mendelian randomization (MR), has since been widely applied. We report the following new contributions. First, we propose a single SNP-based alternative, overcoming a severe limitation of Steiger’s method in simply assuming, instead of inferring, the existence of a causal relationship. We also clarify a condition necessary for the validity of the methods in the presence of hidden confounding. Second, to improve statistical power, we propose combining the results from multiple, and possibly correlated, SNPs as multiple instruments. Third, we develop three goodness-of-fit tests to check modeling assumptions, including those required for valid IVs. Fourth, by relaxing one of the three IV assumptions in MR, we propose several methods, including an Egger regression-like approach and its multivariable version (analogous to multivariable MR), to account for horizontal pleiotropy of the SNPs/IVs, which is often unavoidable in practice. All our methods can simultaneously infer both the existence and (if so) the direction of a causal relationship, largely expanding their applicability over that of Steiger’s method. Although we focus on uni-directional causal relationships, we also briefly discuss an extension to bi-directional relationships. Through extensive simulations and an application to infer the causal directions between low density lipoprotein (LDL) cholesterol, or high density lipoprotein (HDL) cholesterol, and coronary artery disease (CAD), we demonstrate the superior performance and advantage of our proposed methods over Steiger’s method and bi-directional MR. In particular, after accounting for horizontal pleiotropy, our method confirmed the well known causal direction from LDL to CAD, while other methods, including bi-directional MR, might fail.

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“…Second, since MR Steiger's method is based on using only a single SNP as IV, to improve both its statistical power and applicability, we extend it to allow multiple, and possibly correlated, SNPs. In particular, there is increasing interest in applying transcriptome-wide association studies (TWAS, or PrediXcan) to identify causal genes or other molecular/imaging endophenotypes by integrating GWAS with eQTL/xQTL data [20,21,64,58,59,22,47,11,29,60]. In these applications, multiple correlated cis-SNPs near a gene are used as IVs to impute or predict the gene's expression level (or another endophenotype) to infer whether the gene's expression has a causal effect on a trait, say coronary artery disease (CAD); it is assumed that the causal relationship (if existing) is from the gene to the trait.…”

Section: Introductionmentioning

confidence: 99%

Inferring Causal Direction Between Two Traits in the Presence of Horizontal Pleiotropy with GWAS Summary Data

Xue

¹

,

Pan

²

2020

Preprint

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Orienting the causal relationship between pairs of traits is a fundamental task in scientific research with significant implications in practice, such as in prioritizing molecular targets and modifiable risk factors for developing therapeutic and interventional strategies for complex diseases. A recent method, called Steiger’s method, using a single SNP as an instrument variable (IV) in the framework of Mendelian randomization (MR), has since been widely applied. We report the following new contributions. First, we propose a single SNP-based alternative, overcoming a severe limitation of Steiger’s method in simply assuming, instead of inferring, the existence of a causal relationship. We also clarify a condition necessary for the validity of the methods in the presence of hidden confounding. Second, to improve statistical power, we propose combining the results from multiple, and possibly correlated, SNPs. as multiple instruments. Third, we develop three goodness-of-fit tests to check modeling assumptions, including those required for valid IVs. Fourth, by relaxing one of the three IV assumptions in MR, we propose methods, including one Egger regression-like approach and its multivariable version (analogous to multivariable MR), to account for horizontal pleiotropy of the SNPs/IVs, which is often unavoidable in practice. All our methods can simultaneously infer both the existence and (if so) the direction of a causal relationship, largely expanding their applicability over that of Steiger’s method. Although we focus on uni-directional causal relationships, we also briefly discuss an extension to bi-directional relationships. Through extensive simulations and an application to infer the causal directions between low density lipoprotein (LDL) cholesterol, or high density lipoprotein (HDL) cholesterol, and coronary artery disease (CAD), we demonstrate the superior performance and advantage of our proposed methods over Steiger’s method and bi-directional MR. In particular, after accounting for horizontal pleiotropy, our method confirmed the well known causal direction from LDL to CAD, while other methods, including bi-directional MR, failed.Author SummaryIn spite of its importance, due to technical challenges, orienting causal relationships between pairs of traits has been largely under-studied. Mendelian randomization (MR) Steiger’s method has become increasingly used in the last two years. Here we point out several limitations with MR Steiger’s method and propose alternative approaches. First, MR Steiger’s method is based on using only one single SNP as the instrument variable (IV), for which we propose a correlation ratio-based method, called Causal Direction-Ratio, or simply CD-Ratio. An advantage of CD-Ratio is its inference of both the existence and (if so) the direction of a causal relationship, in contrast to MR Steiger’s prior assumption of the existence and its poor performance if the assumption is violated. Furthermore, CD-Ratio can be extended to combine the results from multiple, possibly correlated, SNPs with improved statistical power. Second, we propose two methods, called CD-Egger and CD-GLS, for multiple and possibly correlated SNPs while allowing horizontal pleiotropy. Third, we propose three goodness-of-fit tests to check modeling assumptions for the three proposed methods. Finally, we introduce multivariable CD-Egger, analogous to multivariable MR, as a more robust approach, and an extension of CD-Ratio to cases with possibly bi-directional causal relationships. Our numerical studies demonstrated superior performance of our proposed methods over MR Steiger and bi-directional MR. Our proposed methods, along with freely available software, are expected to be useful in practice for causal inference.

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Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

Cited by 9 publications

References 68 publications

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

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

Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data

Inferring Causal Direction Between Two Traits in the Presence of Horizontal Pleiotropy with GWAS Summary Data

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