Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks

Wolf, Jack M.; Barnard, Martha Underwood; Xia, Xueting; Ryder, Nathan; Westra, Jason; Tintle, Nathan L.

doi:10.1142/9789811215636_0063

Cited by 6 publications

(18 citation statements)

References 21 publications

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“…This approximation method is described in detail in Ray & Boehnke (2018). Two of our previous papers (Gasdaska et al, 2019; Wolf et al, 2020) have demonstrated the accuracy of these three methods through both simulation and real-data applications.…”

Section: Methodsmentioning

confidence: 99%

“…Further, Var. In a recent paper (Wolf et al, 2020), we demonstrated how to calculate these values using only PCSS: and where S ( X ) is the p × p variance-covariance matrix of the columns of the design matrix X , is the p × 1 vector of column means of X , is the mean of w m , and is the sample covariance between w m and x j .…”

Section: Methodsmentioning

confidence: 99%

“…Further, Var(β) =σ 2 (X X) −1 . In a recent paper (Wolf et al, 2020), we demonstrated how to calculate these values using only PCSS:…”

Section: Linear Regression With Covariates Using Pre-computed Summarymentioning

confidence: 99%

“…Previous genome wide association studies have explored the genetic architecture of fatty acids and their ratios (Kalsbeek et al, 2018;Lemaitre et al, 2011;N. L. Tintle et al, 2015;N. Tintle et al, 2020).…”

Section: Fatty-acid Conversion Ratiosmentioning

confidence: 99%

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Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates

Wolf

Westra

Tintle

2021

Preprint

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While the promise of electronic medical record and biobank data is large, major questions remain about patient privacy, computational hurdles, and data access. One promising area of recent development is pre-computing non-individually identifiable summary statistics to be made publicly available for exploration and downstream analysis. In this manuscript we demonstrate how to utilize pre-computed linear association statistics between individual genetic variants and phenotypes to infer genetic relationships between products of phenotypes (e.g., ratios; logical combinations of binary phenotypes using 'and' and 'or') with customized covariate choices. We propose a method to approximate covariate adjusted linear models for products and logical combinations of phenotypes using only pre-computed summary statistics. We evaluate our method's accuracy through several simulation studies and an application modeling various fatty acid ratios using data from the Framingham Heart Study. These studies show consistent ability to recapitulate analysis results performed on individual level data including maintenance of the Type I error rate, power, and effect size estimates. An implementation of this proposed method is available in the publicly available R package pcsstools.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Further, Var(β) =σ 2 (X X) −1 . In a recent paper (Wolf et al, 2020), we demonstrated how to calculate these values using only PCSS:…”

Section: Linear Regression With Covariates Using Pre-computed Summarymentioning

confidence: 99%

Section: Fatty-acid Conversion Ratiosmentioning

confidence: 99%

See 2 more Smart Citations

Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates

Wolf

Westra

Tintle

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method is applicable to both continuous and binary traits. In addition, Wolf et al 2020 proposed an approach for continuous outcomes using summary statistics of outcomes and covariates derived from the same study 6 .…”

Section: Introductionmentioning

confidence: 99%

Approximate conditional phenotype analysis based on genome wide association summary statistics

Wang

Lubitz

et al. 2021

Sci Rep

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Because single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the confounder. However, obtaining conditional results can be time-consuming. We propose an approximate conditional phenotype analysis based on GWAS summary statistics, the covariance between outcome and confounder, and the variant minor allele frequency (MAF). GWAS summary statistics and MAF are taken from GWAS meta-analysis results while the traits covariance may be estimated by two strategies: (i) estimates from a subset of the phenotypic data; or (ii) estimates from published studies. We compare our two strategies with estimates using individual level data from the full GWAS sample (gold standard). A simulation study for both binary and continuous traits demonstrates that our approximate approach is accurate. We apply our method to the Framingham Heart Study (FHS) GWAS and to large-scale cardiometabolic GWAS results. We observed a high consistency of genetic effect size estimates between our method and individual level data analysis. Our approach leads to an efficient way to perform approximate conditional analysis using large-scale GWAS summary statistics.

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Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks

Zigarelli

Venera

Receveur

et al. 2023

Annals of Human Genetics

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As biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.

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Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks

Cited by 6 publications

References 21 publications

Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates

Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates

Approximate conditional phenotype analysis based on genome wide association summary statistics

Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks

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