Genome-Wide Association Study Identifies Risk Loci for Cluster Headache

OBJECTIVE: To identify susceptibility loci for cluster headache and obtain insights into relevant disease pathways. METHODS: We carried out a genome-wide association study, where 852 UK and 591 Swedish cluster headache cases were compared with 5,614 and 1,134 controls, respectively. Following quality control and imputation, single variant association testing was conducted using a logistic mixed model, for each cohort. The two cohorts were subsequently combined in a merged analysis. Downstream analyses, such as gene-set enrichment, functional variant annotation, prediction and pathway analyses, were performed. RESULTS: Initial independent analysis identified two replicable cluster headache susceptibility loci on chromosome 2. A merged analysis identified an additional locus on chromosome 1 and confirmed a locus significant in the UK analysis on chromosome 6, which overlaps with a previously known migraine locus. The lead single nucleotide polymorphisms were rs113658130 (p = 1.92 x 10-17 , OR [95%CI] = 1.51 [1.37-1.66]) and rs4519530 (p = 6.98 x 10-17 , OR = 1.47 [1.34-1.61]) on chromosome 2, rs12121134 on chromosome 1 (p = 1.66 x 10-8 , OR = 1.36 [1.22-1.52]) and rs11153082 (p = 1.85 x 10-8 , OR = 1.30 [1.19-1.42]) on chromosome 6. Downstream analyses implicated immunological processes in the pathogenesis of cluster headache. INTERPRETATION: We identified and replicated several genome-wide-significant associations supporting a genetic predisposition in cluster headache in a genome-wide association study involving 1,443 cases. Replication in larger independent cohorts combined with comprehensive phenotyping, in relation to e.g. treatment response and cluster headache subtypes, could provide unprecedented insights into genotype-phenotype correlations and the pathophysiological pathways underlying cluster headache

Genome-Wide Association Study Identifies Risk Loci for Cluster Headache.

OBJECTIVE: This study was undertaken to identify susceptibility loci for cluster headache and obtain insights into relevant disease pathways. METHODS: We carried out a genome-wide association study, where 852 UK and 591 Swedish cluster headache cases were compared with 5,614 and 1,134 controls, respectively. Following quality control and imputation, single variant association testing was conducted using a logistic mixed model for each cohort. The 2 cohorts were subsequently combined in a merged analysis. Downstream analyses, such as gene-set enrichment, functional variant annotation, prediction and pathway analyses, were performed. RESULTS: Initial independent analysis identified 2 replicable cluster headache susceptibility loci on chromosome 2. A merged analysis identified an additional locus on chromosome 1 and confirmed a locus significant in the UK analysis on chromosome 6, which overlaps with a previously known migraine locus. The lead single nucleotide polymorphisms were rs113658130 (p = 1.92 × 10-17 , odds ratio [OR] = 1.51, 95% confidence interval [CI] = 1.37-1.66) and rs4519530 (p = 6.98 × 10-17 , OR = 1.47, 95% CI = 1.34-1.61) on chromosome 2, rs12121134 on chromosome 1 (p = 1.66 × 10-8 , OR = 1.36, 95% CI = 1.22-1.52), and rs11153082 (p = 1.85 × 10-8 , OR = 1.30, 95% CI = 1.19-1.42) on chromosome 6. Downstream analyses implicated immunological processes in the pathogenesis of cluster headache. INTERPRETATION: We identified and replicated several genome-wide significant associations supporting a genetic predisposition in cluster headache in a genome-wide association study involving 1,443 cases. Replication in larger independent cohorts combined with comprehensive phenotyping, in relation to, for example, treatment response and cluster headache subtypes, could provide unprecedented insights into genotype-phenotype correlations and the pathophysiological pathways underlying cluster headache. ANN NEUROL 2021

Improved gene co-expression network quality through expression dataset down-sampling and network aggregation

International audienceLarge-scale gene co-expression networks are an effective methodology to analyze sets of co-expressed genes and discover new gene functions or associations. Distances between genes are estimated according to their expression profiles and are visualized in networks that may be further partitioned to reveal communities of co-expressed genes. Creating expression profiles is now eased by the large amounts of publicly available expression data (microarrays and RNA-seq). Although many distance calculation methods have been intensively compared and reviewed in the past, it is unclear how to proceed when many samples reflecting a wide range of different conditions are available. Should as many samples as possible be integrated into network construction or be partitioned into smaller sets of more related samples? Previous studies have indicated a saturation in network performances to capture known associations once a certain number of samples is included in distance calculations. Here, we examined the influence of sample size on co-expression network construction using microarray and RNA-seq expression data from three plant species. We tested different down-sampling methods and compared network performances in recovering known gene associations to networks obtained from full datasets. We further examined how aggregating networks may help increase this performance by testing six aggregation methods