Analysis of the progression of systolic blood pressure using imputation of missing phenotype values.

We present a genome-wide association study of a quantitative trait, "progression of systolic blood pressure in time," in which 142 unrelated individuals of the Genetic Analysis Workshop 18 real genotype data were analyzed. Information on systolic blood pressure and other phenotypic covariates was missing at certain time points for a considerable part of the sample. We observed that the dropout process causing missingness is not independent of the initial systolic blood pressure; that is, the data is not missing completely at random. However, after the adjustment for age, the impact of systolic blood pressure on dropouts was no longer significant. Therefore, we decided to impute missing phenotype values by using information from individuals with complete phenotypic data. Progression of systolic blood pressure (∆SBP/∆t) was defined based on the imputed phenotypes and analyzed in a genome-wide fashion. We also conducted an exhaustive genome-wide search for interaction between single-nucleotide polymorphisms (7.14 × 10(10) tests) under an allelic model. The suggested data imputation and the association analysis strategy proved to be valid in the sense that there was no evidence of genome-wide inflation or increased type I error in general. Furthermore, we detected 2 single-nucleotide polymorphisms (SNPs) that met the criterion for genome-wide significance (p≤5 × 10(-8)), which was also confirmed via Monte-Carlo simulation. In view of the rather small sample size, however, the results have to be followed-up in larger studies.

Quick, "imputation-free" meta-analysis with proxy-SNPs.

BACKGROUND: Meta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increase the power to detect strong or weak genotype effects or b) as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming. RESULTS: Here we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying on imputation. This is accomplished by using reference linkage disequilibrium data from 1,000 Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least one study. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Our algorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applications can be used as input files for MA, tremendously speeding up MA compared to the conventional imputation approach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possibly providing an incentive for follow-up studies. We propose our method as a quick screening step prior to imputation-based MA, as well as an additional main approach for studies without available reference data matching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II Diabetes GWAS and found that the proxy algorithm clearly outperforms naïve MA on the p-value level: for 17 out of 23 we observe an improvement on the p-value level by a factor of more than two, and a maximum improvement by a factor of 2127. CONCLUSIONS: YAMAS is an efficient and fast meta-analysis program which offers various methods, including conventional MA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAS can be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats within the GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process.

Integrated genome-wide pathway association analysis with INTERSNP.

OBJECTIVES: Pathway association analysis (PAA) tests for an excess of moderately significant SNPs in genes from a common pathway. METHODS: We present a Monte-Carlo simulation framework that allows to formulate the main ideas of existing PAA approaches using a self-contained rather than a competitive null hypothesis. A stand-alone implementation in INTERSNP makes time-consuming communication with standard GWAS software redundant. By additional parallelization with the OpenMP API, we achieve a reduction in running time for PAA by orders of magnitude, making a power simulation study for PAA feasible. Our approach properly accounts for linkage disequilibrium and is robust with respect to residual λ inflation. RESULTS: We demonstrate that under simple, realistic disease models, PAA can actually strongly outperform the GWAS single-marker approach. PAA methods that make use of the strength of the SNP association (GenGen, Fisher's combination test), in general, perform better than ratio-based methods (ALIGATOR, SNP ratio), whereas the relative performance of gene-based scoring (ALIGATOR, GenGen) and pathway-based scoring (SNP ratio, Fisher's combination test) depends on the architecture of the assumed disease model. Finally, we present a new PAA score that models independent signals from the same gene in a regression framework and show that it is a reasonable compromise that combines the advantages of existing ideas.