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HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics.
Jie Zheng1,2, Santiago Rodriguez1,2, Charles Laurin1,2
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.
Bioinformatics (Oxford, England)
|September 4, 2016
Summary
Fine mapping using summary statistics is improved by HAPRAP, a novel method utilizing haplotype information. This approach enhances accuracy and robustness, especially with limited data or low-frequency variants.
Area of Science:
- Genetics and Genomics
- Statistical Genetics
- Bioinformatics
Background:
- Fine mapping identifies causal variants at disease-associated loci, traditionally requiring individual-level genotype data.
- Existing summary-level fine mapping methods rely on pairwise correlation coefficients, which are less biologically representative than haplotypes.
- Haplotypes, not pairwise correlations, accurately reflect linkage disequilibrium (LD) among multiple loci.
Purpose of the Study:
- To introduce the Haplotype Regional Association analysis Program (HAPRAP), a novel empirical iterative method for genetic fine mapping.
- To enable fine mapping using readily available summary statistics and haplotype information from reference panels.
- To overcome limitations of existing methods by leveraging haplotype structure for improved accuracy and robustness.
Main Methods:
- Developed HAPRAP, an empirical iterative method for fine mapping.
- Utilizes summary statistics and haplotype information from an individual-level reference panel.
- Evaluated performance through simulations with individual-level and summary-level data, and a parametric simulation using human height data.
Main Results:
- HAPRAP demonstrated high consistency with multiple regression on individual-level genotypes.
- In simulations with summary-level data, HAPRAP showed reduced sensitivity to inaccurate LD estimates.
- HAPRAP performed well with small sample sizes (<2000) and was robust to single nucleotide polymorphisms (SNPs) with low minor allele frequencies.
- Applied to meta-analyses of human height, QTc interval, and gallbladder disease, HAPRAP replicated known associations and identified two novel variants for human height.
Conclusions:
- HAPRAP provides a robust and accurate method for genetic fine mapping using summary statistics and haplotype information.
- The method is particularly valuable given the increasing availability of summary-level data for large-scale genetic studies.
- HAPRAP is expected to facilitate future applications such as functional prediction and the identification of instruments for Mendelian randomization.
