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 Test1) 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.
Highlights
Genome-wide association studies (GWASs) have been widely applied with the goals to detect genetic variants which contribute to complex traits in the past decade (McCarthy et al, 2008)
GWASs have led to abundant significant findings (Easton et al, 2007; Hakonarson et al, 2007; Parkes et al, 2007; Zeggini et al, 2007; Thomas et al, 2008), a few practical difficulties hinder the discovery of more rare or low-frequency genetic variants
The large public datasets, gnomad.genome.NFE (Non-Finnish European, N = 7509) (Lek et al, 2016) was selected as reference to compute the p-value of our model
Summary
Genome-wide association studies (GWASs) have been widely applied with the goals to detect genetic variants which contribute to complex traits in the past decade (McCarthy et al, 2008). Allele frequencies of genetic variants are compared between cases that are supposed to have a high prevalence of susceptibility alleles and controls that are considered to have a lower prevalence of such alleles. GWASs have led to abundant significant findings (Easton et al, 2007; Hakonarson et al, 2007; Parkes et al, 2007; Zeggini et al, 2007; Thomas et al, 2008), a few practical difficulties hinder the discovery of more rare or low-frequency genetic variants. Reference Association Test of GWAS the latent genetic variants (Wellcome Trust Case Control Consortium, 2007; He et al, 2009). Difference in genetic background, known as population stratification, between cases and controls could inflate type I error rate, thereby, leading to increasing level of false positive findings (Bacanu et al, 2000)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
