Using propensity score adjustment method in genetic association studies. (June 2016)

• Record Type: Journal Article
• Authors: Sengupta Chattopadhyay, Amrita; Lin, Ying-Chao; Hsieh, Ai-Ru; Chang, Chien-Ching; Lian, Ie-Bin; Fann, Cathy S.J.
• Is Part Of: Computational biology and chemistry. Volume 62(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-06
• Subjects: PS propensity Score -- PSAM propensity score adjustment method -- ULRM univariate logistic regression method -- S-MLRM stepwise-multivariate logistic regressioin method -- GWAS Genome Wide Association studies -- GAW genetic analysis workshop -- NARAC North American Rheumatoid Arthritis Consortium -- SNP single nucleotide polymorphism -- CPU Central Processing Unit -- LD linkage disequilibrium -- Dom dominant -- Rec recessive -- MDR multifactor dimensionality reduction -- ATM Ataxia telangiectasia mutated -- PTPN22 protein tyrosine phosphatase, non-receptor type 22 lymphoid -- IPA ingenuity pathway analysis -- DNA deoxyribo-nucleic acid -- QC quality control -- PSM propensity score matching -- SPS stratified propensity score -- PC principle component -- PCA principle component analysis; Propensity score -- Logistic regression -- Single-point association test -- Gene–gene interaction -- Rheumatoid arthritis -- Single nucleotide polymorphism; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.02.017
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: The proposed propensity score adjustment method (PSAM) is a tool to improve power for single locus association studies through an estimated propensity-score (PS) by adjusting for SNPs that might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP while regressing disease status on target SNP. The degree of freedom is always restricted to 1. Highlights: Propensity score adjustment method (PSAM) is proposed as a tool for dimension reduction to improve the power for single locus studies through an estimated propensity score (PS). PS is used to adjust for the effect of SNPs that influence the marginal association of a candidate marker. PS adjusts for influence from these SNPs while regressing disease status on the target-genetic locus. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. PSAM was able to identify significant SNPs from the GAW16 NARAC dataset by reducing the original trend-test p -values which were further found to be associated with immunity and inflammation. Abstract: Background: The statistical tests for single locus disease association are mostly under-powered. If a disease associated causal single nucleotide polymorphism (SNP) operates essentially through a complex mechanism that involves multiple SNPs or possible environmental factors, its effect might be missed if the causal SNP is studied in isolation without accounting for these unknown genetic influences. In this study, we attempt to address the issue of reduced power that is inherent in single point association studies by accounting for genetic influences that negatively impact the detection of causal variant in single point association analysis. In our method we use propensity score (PS) to adjust for the effect of SNPs that influence the marginal association of a candidate marker. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. We therefore propose a propensity score adjustment method (PSAM) as a tool for dimension reduction to improve the power for single locus studies through an estimated PS to adjust for influence from these SNPs while regressing disease status on the target-genetic locus. The degree of freedom of such a test is therefore always restricted to 1. Results: We assess PSAM under the null hypothesis of no disease association to affirm that it correctly controls for the type-I-error rate (<0.05). PSAM displays reasonable power (>70%) and shows an average of 15% improvement in power as compared with commonly-used logistic regression method and PLINK under most simulated scenarios. Using the open-access multifactor dimensionality reduction dataset, PSAM displays improved significance for all disease loci. Through a whole genome study, PSAM was able to identify 21 SNPs from the GAW16 NARAC dataset by reducing their original trend-test p -values from within 0.001 and 0.05 to p -values less than 0.0009, and among which 6 SNPs were further found to be associated with immunity and inflammation. Conclusions: PSAM improves the significance of single-locus association of causal SNPs which have had marginal single point association by adjusting for influence from other SNPs in a dataset. This would explain part of the missing heritability without increasing the complexity of the model due to huge multiple testing scenarios. The newly reported SNPs from GAW16 data would provide evidences for further research to elucidate the etiology of rheumatoid arthritis. PSAM is proposed as an exploratory tool that would be complementary to other existing methods. A downloadable user friendly program, PSAM, written in SAS, is available for public use.