Genetic variants associated with response to anti-CGRP monoclonal antibody therapy in a chronic migraine Han Chinese population

Abstract

Background: Anti-calcitonin gene-related peptide (CGRP) monoclonal antibodies have emerged as promising therapeutic options for the treatment of chronic migraine. However, treatment response varies considerably among individuals, suggesting a potential role for genetic factors. This study aimed to identify genetic variants affecting the efficacy of anti-CGRP monoclonal antibody therapy in chronic migraine among the Han Chinese population in Taiwan to enhance treatment precision and to understand the genetic architecture of migraine.

Methods: We conducted a quantitative trait locus (QTL) association study in patients with chronic migraines from a tertiary medical center in Taiwan using the Taiwan Precision Medicine Array Chip. The patients received fremanezumab or galcanezumab for at least 12 weeks. Treatment efficacy was assessed based on the improvement rate in monthly migraine days. Genetic variants were identified, and their associations with treatment efficacy were examined through quantitative trait loci analysis, linkage disequilibrium studies, and functional annotations using the Gene Ontology database.

Results: Six single nucleotide polymorphisms (SNPs) relative variants were significantly associated with anti-CGRP therapy response (p < 1 × 10^{- 7}): rs116870564, rs75244870, rs56216870, rs12938101, rs74655790, and rs149540851. These variants are located in or near genes, including LRRC4C, ATAD2B, and OXR1, which are involved in neuronal development, DNA-dependent ATPase activity, and oxidation-reduction processes, respectively. The rs116870564 variant in LRRC4C showed the strongest association (β = -0.551, p = 6.65 × 10^{- 9}). The functional impact of these variants is attributed to their regulatory effects on gene expression, which are influenced by intron splicing regulation, transcription factors, and changes in chromatin structure.

Conclusion: The identification of key genetic markers associated with response to anti-CGRP therapy emphasizes the importance of genetic variability in treatment efficacy. This could lead to more personalized chronic migraine management strategies and tailored therapeutic approaches based on individual genetic profiles. Further research in larger, diverse populations is warranted to validate these findings and refine our understanding of the role of CGRP in chronic migraine pathophysiology.

Trial registration: Not applicable.

Keywords: Chronic migraine; Genetic variants; Personalized medicine; SNP genotyping; Treatment response; anti-CGRP monoclonal antibodies.

Fig. 1

Study Flowchart. This flowchart outlines the steps involved in this study, including participant recruitment and data analysis. The workflow included genotyping using the Taiwan Precision Medicine array, quantitative trait locus analysis, and subsequent steps, such as Gene Ontology enrichment and linkage disequilibrium analysis. Key elements include SNP filtering, application of the linear regression model using PLINK 1.9 software, and systematic exploration of covariate combinations to identify significant variants associated with the anti-CGRP monoclonal antibody therapy response in patients with chronic migraine

Fig. 2

Quantitative trait locus (QTL) analysis results for anti-CGRP monoclonal antibody therapy response in chronic patients with migraines. (A) Circular Manhattan plot showing the QTL association results. Each point represents a SNP, with its position on the respective chromosome indicated by the outer circle. The inner circle shows the statistical significance (-log10 p-value) of each SNP’s association with treatment response. The blue dashed line indicates the threshold for significance (p < 1 × 10^− 7). Six highly significant variants are highlighted and labeled: rs116870564, rs75244870, rs56216870, rs12938101, rs74655790, and rs149540851. (B) Quantile-Quantile (Q-Q) plot of the observed versus expected -log10 p-values from the association analysis. The red line represents the null hypothesis of no association. Deviations above this line indicate potential true genetic associations with the improvement rate. The genomic inflation factor (λ) is provided, calculated based on the median of observed and expected chi-square values, demonstrating the robustness of the test statistics

Fig. 3

Genotype-phenotype associations for significant variants identified through quantitative trait locus analysis. Box plots showing the association between the genotypes of six significant SNPs (rs116870564, rs75244870, rs56216870, rs12938101, rs74655790, and rs149540851) and improvement rates in monthly migraine days after anti-CGRP monoclonal antibody therapy. For each variant, the distribution of improvement rates is shown for the different genotypes. The heterozygous mutations AG, TC, and AC in rs116870564, rs74655790, and rs149540851, respectively, and the homozygous mutation TT in rs56216870 showed a strong decrease in improvement rates compared to the normal genotypes. Boxes represent the interquartile range, with the median indicated by a horizontal line. Whiskers extend to minimum and maximum values, excluding outliers

Fig. 4

Linkage disequilibrium (LD) analysis of intronic variants. LD plots and functional annotations for the three significant intronic variants: (A) rs116870564 in LRRC4C (chromosome 11). (B) rs74655790 in ATAD2B (chromosome 2). (C) rs149540851 in OXR1 (chromosome 8). Each panel shows the LD structure around focal SNP. The color gradient, ranging from white to deep blue and red, visualizes the spectrum of D’ (D prime) and R² (R-squared) values spanning from 0.0 to 1.0 in the relative chromosome region. White numbers in the purple squares represent FORGEdb scores, indicating the likelihood of variants functioning as regulatory elements. The red text indicates the query intronic variants from the quantitative trait locus analysis. The genes in this region are shown below each plot and thin lines represent intron regions and bold lines indicate exon regions