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Genetic Variant Detection in the CALR gene using High Resolution Melting Analysis

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Published on: August 26, 2020

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution.

Masahiro Kanai^1,2,3,4,5,6, Roy Elzur^1,2,3, Wei Zhou^1,2,3

¹Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02142, USA.

|January 16, 2023

Summary

Heterogeneity in genome-wide association study (GWAS) meta-analyses can compromise fine-mapping accuracy. A new quality control method, SLALOM, identifies suspicious loci, revealing widespread issues and questioning the reliability of current meta-analysis fine-mapping results.

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Area of Science:

Genetics
Bioinformatics
Statistical Genetics

Background:

Meta-analysis is a common method for combining genome-wide association studies (GWASs).
Fine-mapping in meta-analysis studies is typically approached similarly to single-cohort studies.
Heterogeneity across cohorts (e.g., sample size, phenotyping, imputation) can impact the accuracy of meta-analysis fine-mapping.

Purpose of the Study:

To demonstrate the detrimental effect of heterogeneity on meta-analysis fine-mapping calibration.
To introduce a novel quality control (QC) method for identifying suspicious loci in meta-analysis fine-mapping.
To assess the accuracy and reliability of fine-mapping results from large-scale GWAS meta-analyses.

Main Methods:

Developed a summary statistics-based QC method named Suspicious Loci Analysis of Meta-analysis Summary Statistics (SLALOM).
SLALOM detects outliers in association statistics to identify suspicious loci.
Validated SLALOM using simulations and the GWAS Catalog, and applied it to 14 Global Biobank Meta-analysis Initiative (GBMI) meta-analyses.

Main Results:

Heterogeneity was shown to negatively affect meta-analysis fine-mapping calibration.
SLALOM identified suspicious patterns in 67% of loci across 14 GBMI meta-analyses, questioning fine-mapping accuracy.
Suspicious loci were significantly less likely to have nonsynonymous variants as lead variants (2.7× depletion).
Limited improvement in fine-mapping was observed in GBMI meta-analyses compared to individual biobanks.

Conclusions:

Heterogeneity poses a significant challenge to the accuracy of fine-mapping in GWAS meta-analyses.
The SLALOM method effectively identifies loci requiring cautious interpretation.
Extreme caution is advised when interpreting fine-mapping results derived from meta-analyses of heterogeneous cohorts.