A genome-wide association study in individuals of African ancestry reveals the importance of the Duffy-null genotype in the assessment of clozapine-related neutropenia.

Individuals of African ancestry in the United States and Europe are at increased risk of developing schizophrenia and have poorer clinical outcomes. The antipsychotic clozapine, the only licensed medication for treatment-resistant schizophrenia, is under-prescribed and has high rates of discontinuation in individuals of African ancestry, due in part to increased rates of neutropenia. The genetic basis of lower neutrophil levels in those of African ancestry has not previously been investigated in the context of clozapine treatment. We sought to identify risk alleles in the first genome-wide association study of neutrophil levels during clozapine treatment, in 552 individuals with treatment-resistant schizophrenia and robustly inferred African genetic ancestry. Two genome-wide significant loci were associated with low neutrophil counts during clozapine treatment. The most significantly associated locus was driven by rs2814778 (ß = -0.9, P = 4.21 × 10-21), a known regulatory variant in the atypical chemokine receptor 1 (ACKR1) gene. Individuals homozygous for the C allele at rs2814778 were significantly more likely to develop neutropenia and have to stop clozapine treatment (OR = 20.4, P = 3.44 × 10-7). This genotype, also termed "Duffy-null", has previously been shown to be associated with lower neutrophil levels in those of African ancestry. Our results indicate the relevance of the rs2814778 genotype for those taking clozapine and its potential as a pharmacogenetic test, dependent on the outcome of additional safety studies, to assist decision making in the initiation and on-going management of clozapine treatment.

Evaluation of an approximation method for assessment of overall significance of multiple-dependent tests in a genomewide association study.

We describe implementation of a set-based method to assess the significance of findings from genomewide association study data. Our method, implemented in PLINK, is based on theoretical approximation of Fisher's statistics such that the combination of P-vales at a gene or across a pathway is carried out in a manner that accounts for the correlation structure, or linkage disequilibrium, between single nucleotide polymorphisms. We compare our method to a permutation-based product of P-values approach and show a typical correlation in excess of 0.98 for a number of comparisons. The method gives Type I error rates that are less than or equal to the corresponding nominal significance levels, making it robust to the effects of false positives. We show that in broadly similar populations, reference data sets of markers are an appropriate substrate for deriving marker-marker linkage disequilibrium (LD), negating the need to access individual level genotypes, greatly facilitating its generic applicability. We show that the method is thus robust to LD-associated bias and has equivalent performance to permutation-based methods, with a significantly shorter runtime. This is particularly relevant at a time of increasing public availability of significantly larger genetic data sets and should go a long way to assist in the rapid analysis of these data sets.