Statistical methods to detect novel genetic variants using publicly available GWAS summary data

Gonzales

Liu

et al. 2019

Preprint

Gene-based analyses offer a useful alternative and complement to the usual single nucleotide polymorphism (SNP) based analysis for genome-wide association studies (GWASs). Using appropriate weights (pre-specified or eQTL-derived) can boost statistical power, especially for detecting weak associations between a gene and a trait. Because the sparsity level or association directions of the underlying association patterns in real data are often unknown and access to individual-level data is limited, we propose an optimal weighted combination (OWC) test 1 applicable to summary statistics from GWAS. This method includes burden tests, weighted sum of squared score (SSU), weighted sum statistic (WSS), and the score test as its special cases. We analytically prove that aggregating the variants in one gene is the same as using the weighted combination of Z-scores for each variant based on the score test method. We also numerically illustrate that our proposed test outperforms several existing comparable methods via simulation studies. Lastly, we utilize schizophrenia GWAS data and a fasting glucose GWAS meta-analysis data to demonstrate that our method outperforms the existing methods in real data analyses. Our proposed test is implemented in the R program OWC, which is freely and publicly available.

Section: Comparison Of Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimally Weighted Combination Method to Detect Novel Disease Associated Genes Using Publicly Available GWAS Summary Data

Gonzales

Liu

et al. 2019

Preprint

“…Currently, there are three types of methods utilizing summary statistics including burden, quadratic and adaptive forms (Pasaniuc and Price, 2017). The three methods proposed by Guo and Wu (2018): sum test (ST), squared sum test (S2T) and adaptive test (AT) are good representative of the three types of methods. Therefore, we compare the performance of the proposed method (GPA) with the three methods.…”

Section: Comparison Of Methodsmentioning

confidence: 99%

“…Figure 1 shows the Venn diagram comparing the number of significant genes identified by the proposed test with Guo and Wu's method. The three methods of Guo and Wu (2018) identified 25 significant genes in total, specifically, the ST identified 12 significant genes, S2T…”

Section: Zipmentioning

confidence: 99%

See 1 more Smart Citation

Powerful statistical method to detect disease associated genes using publicly available GWAS summary data

Guo

et al. 2018

Preprint

Self Cite

Genome-wide association studies (GWAS) have thus far achieved substantial success.In the last decade a large number of common variants underlying complex diseases have been identified through GWAS. In most existing GWAS, the identified common variants are obtained by single marker based tests, that is, testing one single nucleotide polymorphisms (SNP) at a time. Generally the basic functional unit of inheritance is a gene, rather than a SNP. Thus, results from gene level association test can be more readily integrated with downstream functional and pathogenic investigation. In this paper, we propose a general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple genetic variants using only GWAS summary statistics (either p-value or other test statistics). The proposed method could be used to test both continuous and binary traits through not only a single but also multiple studies, which helps overcome the limitation of existing methods that only can be applied to specific type of data.We conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We demonstrated the utility of our proposed method through analysis of GWAS meta-analysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel traits associated genes which can improve our understanding of the mechanisms involved in β-cell function, glucose homeostasis and lipids traits.

Powerful statistical method to detect disease‐associated genes using publicly available genome‐wide association studies summary data

Zhao

Guo

et al. 2019

Genetic Epidemiology

Self Cite

Genome-wide association studies (GWAS) have thus far achieved substantial success. In the last decade, a large number of common variants underlying complex diseases have been identified through GWAS. In most existing GWAS, the identified common variants are obtained by single marker-based tests, that is, testing one single-nucleotide polymorphism (SNP) at a time. Generally, the basic functional unit of inheritance is a gene, rather than a SNP. Thus, results from gene-level association test can be more readily integrated with downstream functional and pathogenic investigation. In this paper, we propose a general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple genetic variants using only GWAS summary statistics (either p-value or other test statistics). The proposed method could be used to test genetic association for both continuous and binary traits through not only one study but also multiple studies, which would be helpful to overcome the limitation of existing methods that can only be applied to a specific type of data. We conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We demonstrated the utility of our proposed method through analysis of GWAS metaanalysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel trait associated genes which can improve our understanding of the mechanisms involved in β-cell function, glucose homeostasis, and lipids traits. K E Y W O R D Sassociation test, genome-wide association study, meta-analysis, summary data