Robust Reference Powered Association Test of Genome-Wide Association Studies

Abstract

Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test 1 ) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals.

On the Different Ways to Handle the Trend of Disease Risk in Genetic Association Tests

“…The test statistic defines (Equation ( 1)) the summation over all the six cells of the table , where, O i represents the observed cell counts for each of the six cells: n 1 , n 2 , n 3 , n 4 , n 5 , n 6 . Here, under the null hypothesis of no association, the test statistic compares the observed number of M/M genotypes in cases with the corresponding expected assuming that the relative allele (or genotype frequencies) to be the same in case and control groups for the M/M genotype [4] [20].…”

Atrial Fibrillation Genetics Update: Toward Clinical Implementation

“…However, the power of a GWAS depends not only on the sample size, but equally important are the genetic coverage (genotyping chip and imputation panel density), the frequency of the genetic variants, the ancestry group, and the effect size (44–48). Nelson et al have estimated the power of GWAS across a range of genotyping chips, ancestry groups, minor allele frequencies, and effect sizes, suggesting that we are close to having identified the most relevant common variants for AF in European ancestry groups.…”