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Singh, A. ; Southam, L. ; Hatzikotoulas, K. ; Rayner, N.W. ; Suzuki, K.* ; Taylor, H.J.* ; Yin, X.* ; Mandla, R.* ; Huerta-Chagoya, A.* ; Morris, A.P. ; Zeggini, E. ; Bocher, O.

Correcting for genomic inflation leads to loss of power in large-scale Genome-wide association study meta-analysis.

Genet. Epidemiol. 49:e70016 (2025)

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Inflation in genome-wide association studies (GWAS) summary statistics represents a major challenge, for which correction methods have been developed. These include the genomic control (GC) method, which uses the λ-value to correct summary statistics, and the linkage disequilibrium score regression (LDSR) method, which uses the LDSR intercept. By using type 2 diabetes (T2D) as an exemplar, we explore factors influencing λ-values and the impact of these corrections on association signals. We find that larger sample sizes increase λ-values due to increased captured polygenicity, while including lower frequency variants decreases λ-values due to reduced power. Comparing T2D genetic associations described in overlapping GWAS meta-analyses of increasing sample size, we find that GC correction reduces the false positive rate and leads to the loss of robust associations. In one of the largest meta-analysis, GC correction results in 39.7% loss of independent loci, substantially reducing the number of detected associations. In comparison, the LDSR intercept correction leads to a loss of up to 25.2% of the independent loci, being therefore less conservative than the GC correction. We conclude that in large, well-powered GWAS meta-analysis of polygenic traits, both GC and LDSR intercept correction leads to power loss, highlighting the need for improved genomic inflation correction methods.

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