Abstract
BackgroundUnderstanding the mapping precision of genome-wide association studies (GWAS), that is the physical distances between the top associated single-nucleotide polymorphisms (SNPs) and the causal variants, is essential to design fine-mapping experiments for complex traits and diseases.ResultsUsing simulations based on whole-genome sequencing (WGS) data from 3642 unrelated individuals of European descent, we show that the association signals at rare causal variants (minor allele frequency ≤ 0.01) are very unlikely to be mapped to common variants in GWAS using either WGS data or imputed data and vice versa. We predict that at least 80% of the common variants identified from published GWAS using imputed data are within 33.5 Kbp of the causal variants, a resolution that is comparable with that using WGS data. Mapping precision at these loci will improve with increasing sample sizes of GWAS in the future. For rare variants, the mapping precision of GWAS using WGS data is extremely high, suggesting WGS is an efficient strategy to detect and fine-map rare variants simultaneously. We further assess the mapping precision by linkage disequilibrium between GWAS hits and causal variants and develop an online tool (gwasMP) to query our results with different thresholds of physical distance and/or linkage disequilibrium (http://cnsgenomics.com/shiny/gwasMP).ConclusionsOur findings provide a benchmark to inform future design and development of fine-mapping experiments and technologies to pinpoint the causal variants at GWAS loci.
Highlights
Understanding the mapping precision of genome-wide association studies (GWAS), that is the physical distances between the top associated single-nucleotide polymorphisms (SNPs) and the causal variants, is essential to design fine-mapping experiments for complex traits and diseases
The simulations were based on whole-genome sequencing (WGS) data on 3642 unrelated individuals and ~17.6 million genetic variants from the UK10K project [7] after quality controls (QC)
Wu et al Genome Biology (2017) 18:86 simulation replicate, we randomly sampled a sequence variant as causal variant to generate a phenotype and performed genome-wide association analyses of the simulated phenotype using genotype data from four different genotyping/imputation strategies: (1) WGS data; (2) SNP-array data imputed to HapMap phase 2 [8] (HapMap2); (3) SNP-array data imputed to 1000 Genomes Project [9] (1KGP) phase 1 (1KGP1); (4) SNP-array data imputed to 1KGP phase 3 (1KGP3)
Summary
Understanding the mapping precision of genome-wide association studies (GWAS), that is the physical distances between the top associated single-nucleotide polymorphisms (SNPs) and the causal variants, is essential to design fine-mapping experiments for complex traits and diseases. There are a few studies that have been able to pinpoint the causal variant and/or the functional gene(s) at a GWAS locus [2,3,4,5] These examples, are rare to date, and high-throughput experiments and technologies are in high demand to fine-map the causal variants and/or genes at the GWAS loci [6]. Understanding the distribution of the distances between the top associated variants in GWAS and the underlying causal variants is essential to design and develop such fine-mapping experiments and technologies. We seek to quantify the empirical distribution of physical distances between GWAS hits and causal variants for different genotyping strategies using simulations
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