Adjustment for covariates using summary statistics of genome‐wide association studies

Wang, Tao; Xue, Xiaonan; Xie, Xianhong; Ye, Kenny; Zhu, Xiaofeng; Elston, Robert C.

doi:10.1002/gepi.22148

Cited by 6 publications

(6 citation statements)

References 34 publications

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“…For the subject matter of the presentation, the covariates for GWAS are divided into three categories: I ) covariates without or of little heritability but of biological significance, such as sex [17]; II ) covariates with heritability, such as height and BMI, which are known to influence the outcome of GWAS due to genetic correlation [6, 18]; III ) covariates for population structure, surrogated by principal components [19, 20, 21]. We demonstrate in traits Standing height and Weight (UKB field ID: 21002) how UKC provides additional information than a conventional GWAS ( Fig.…”

Section: Resultsmentioning

confidence: 99%

UK BioCoin: Swift Trait-Specific Summary Statistics Regression for UK Biobank

He,

Qi,

Ying

et al. 2024

Preprint

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Summary statistics derived from large-scale biobanks facilitate the sharing of genetic discoveries while minimizing the risk of compromising individual-level data privacy. However, these summary statistics, such as those from the UK Biobank (UKB) provided by Neale's lab, are often adjusted by a fixed set of covariates to all traits (12 covariates including 10 PCs, sex and age), preventing the exploration of trait-specific summary statistics. In this study, we present a novel computational device UK BioCoin (UKC), which is designed to provide an efficient framework for trait-specific adjustment for covariates. Without requiring access to individual-level data from UKB, UKC leverages summary statistics regression technique and resources from UKB (289 GB of 199 phenotypes and 10 million SNPs), to enable the generation of GWAS summary statistics adjusted by user-specified covariates. Through a comprehensive analysis of height under trait-specific adjustments, we demonstrate that the GWAS summary statistics generated by UKC closely mirror those generated from individual-level UKB GWAS (ρ > 0.99 for effect sizes and ρ > 0.99 for p-values). Furthermore, we demonstrate the results for GWAS, SNP-heritability estimation, polygenic score, and Mendelian randomization, after various trait-specific covariate adjustments as allowed by UKC, indicating UKC a platform that harnesses in-depth exploration for researchers lacking access to UKB. The whole framework of UKC is portable for other biobank, as demonstrated in Westlake Biobank, which can equivalently be converted to a `UKC-like" platform and promote data sharing. UKC has its computational engine fully optimized, and the computational efficiency of UKC is about 70 times faster than that of UKB. We package UKC as a Docker image of 20 GB (https://github.com/Ttttt47/UKBioCoin), which can be easily deployed on an average computer (e.g. laptop).

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Section: Resultsmentioning

confidence: 99%

UK BioCoin: Swift Trait-Specific Summary Statistics Regression for UK Biobank

He,

Qi,

Ying

et al. 2024

Preprint

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“…Indeed, the effectiveness of haplotype matching depends on local LD, but such associations make it very difficult to distinguish causal SNPs from hitch-hikers. Second, GWAS but not gSCA routinely include environmental factors as covariates to statistically remove their effects [51]. gSCA models can be written to include covariates, but with a focus on natural populations, one may not wish to factor out environmental effects.…”

Section: Discussionmentioning

confidence: 99%

The promise and deceit of genomic selection component analyses

Kelly

2021

Proc. R. Soc. B.

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Selection component analyses (SCA) relate individual genotype to fitness components such as viability, fecundity and mating success. SCA are based on population genetic models and yield selection estimates directly in terms of predicted allele frequency change. This paper explores the statistical properties of gSCA: experiments that apply SCA to genome-wide scoring of SNPs in field sampled individuals. Computer simulations indicate that gSCA involving a few thousand genotyped samples can detect allele frequency changes of the magnitude that has been documented in field experiments on diverse taxa. To detect selection, imprecise genotyping from low-level sequencing of large samples of individuals provides much greater power than precise genotyping of smaller samples. The simulations also demonstrate the efficacy of ‘haplotype matching’, a method to combine information from a limited collection of whole genome sequence (the reference panel) with the much larger sample of field individuals that are measured for fitness. Pooled sequencing is demonstrated as another way to increase statistical power. Finally, I discuss the interpretation of selection estimates in relation to the Beavis effect, the overestimation of selection intensities at significant loci.

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“…With the adjusted and unadjusted information at hand, we can potentially estimate the bias of including the covariate and interpret the GWAS results more cautiously 12 . For continuous outcomes, Wang et al 13 provided corrections to filter potentially spurious associations (i.e., false positive associations) using GWAS summary statistics. We utilized their approach when applying our proposed method and removed more than 100 variants which might be false positives in the WC GWAS adjusted for BMI in the FHS (Supplemental Fig.…”

Section: Discussionmentioning

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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Adjustment for covariates using summary statistics of genome‐wide association studies

Cited by 6 publications

References 34 publications

UK BioCoin: Swift Trait-Specific Summary Statistics Regression for UK Biobank

UK BioCoin: Swift Trait-Specific Summary Statistics Regression for UK Biobank

The promise and deceit of genomic selection component analyses

Approximate conditional phenotype analysis based on genome wide association summary statistics

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