Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data

Wu, Yang, Zheng, Zhili, Visscher, Peter M. and Yang, Jian (2017) Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data. Genome Biology, 18 1: 86-86. doi:10.1186/s13059-017-1216-0


Author Wu, Yang
Zheng, Zhili
Visscher, Peter M.
Yang, Jian
Title Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data
Journal name Genome Biology   Check publisher's open access policy
ISSN 1474-760X
1474-7596
Publication date 2017-05-16
Year available 2017
Sub-type Article (original research)
DOI 10.1186/s13059-017-1216-0
Open Access Status DOI
Volume 18
Issue 1
Start page 86
End page 86
Total pages 10
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Abstract 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.
Formatted abstract
Background: 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.

Results: Using 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 ).

Conclusions: Our findings provide a benchmark to inform future design and development of fine-mapping experiments and technologies to pinpoint the causal variants at GWAS loci.
Keyword False positive rate
Genome-wide association studies
Imputation
Mapping precision
Whole genome sequencing
Q-Index Code C1
Grant ID DP160101343
1078037
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
Institute for Molecular Bioscience - Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 1 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Mon, 05 Jun 2017, 01:00:44 EST by Web Cron on behalf of Learning and Research Services (UQ Library)