Regionally Smoothed Meta-Analysis Methods for GWAS Datasets

Abstract

Genome-wide association studies (GWAS) are proven tools for finding disease genes, but it is often necessary to combine many cohorts into a meta-analysis to detect statistically significant genetic effects. Often the component studies are performed by different investigators on different populations, using different chips with minimal SNPs overlap. In some cases, raw data are not available for imputation so that only the genotyped SNP results can be used in meta-analysis. Even when SNP sets are comparable, different cohorts may have peak association signals at different SNPs within the same gene due to population differences in linkage disequilibrium or environmental interactions. We hypothesize that the power to detect statistical signals in these situations will improve by using a method that simultaneously meta-analyzes and smooths the signal over nearby markers. In this study we propose regionally smoothed meta-analysis (RSM) methods and compare their performance on real and simulated data.

A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

“…The performances of SEM-BayesCΠ and SEM-GWAS were compared based on the AUC (Area under Receiver Operating Characteristic). Inference of association on genomic windows for SEM-GWAS was based on the minimum p-value (Begum et al 2016), i.e., genomic windows containing at least one significant variant are declared as significant windows. To exclude the irrelevant AUC with low levels of specificity, only the partial area under the curve up until the false positive rate of 5% (pAUC5) (Chen et al 2017;Ma et al 2013) was calculated.…”

“…The performances of SEM-BayesC and SEM-GWAS were compared based on the AUC (Area under Receiver Operating Characteristic). Inference of association on genomic windows for SEM-GWAS was based on the minimum p-value ( Begum et al 2016 ), i.e. , genomic windows containing at least one significant variant are declared as significant windows.…”

Genomics of altitude-associated wing shape in two tropical butterflies

“…When visualising our results, we first use median p-value per window, plotted as -log10(p-value), to regionally smooth the results so that spurious associations get dampened by their flanking high p-value SNPs, while regions with many SNPs with strong associations will be easily identifiable, as their median will remain high. This is analogous to recently developed methods for medical genetics that use penalized moving-window regressions (Bao & Wang, 2017;Begum, Sharker, Sherman, Tseng, & Feingold, 2016;Braz et al, 2019;C. Chen, Steibel, & Tempelman, 2017) or LD clumping (Marees et al, 2018).…”

Advances in the genome-wide association study of chronic hepatitis B susceptibility in Asian population

“…However, such effect on the complex disease is generally very weak, therefore this strategy cannot be simply taken to investigate the causes of complex diseases. Hence, how to find an effective way to perform in-depth analysis for GWAS data and then detect more susceptibility genes has become a new research hotspot [ 30 ]. To date, different strategies and approaches have been successively taken in the follow-up studies of GWAS for complex diseases in order to perform in-depth data mining.…”