Big data challenges in bone research: genome-wide association studies and next-generation sequencing

Independent and Combined Associations of Sleep Duration, Bedtime, and Polygenic Risk Score with the Risk of Hearing Loss among Middle-Aged and Old Chinese: The Dongfeng-Tongji Cohort Study

Evidence available on the independent and combined associations of sleep duration, bedtime, and genetic predisposition with hearing loss was lacking. The present study included 15,827 participants from the Dongfeng-Tongji cohort study. Genetic risk was characterized by polygenic risk score (PRS) based on 37 genetic loci related to hearing loss. We conducted multivariate logistic regression models to assess the odds ratio (OR) for hearing loss with sleep duration and bedtime, as well as the joint association and interaction with PRS. Results showed that hearing loss was independently associated with sleeping ≥9 h/night compared to the recommended 7 to <8 h/night, and with bedtime ≤9:00 p.m. and >9:00 p.m. to 10:00 p.m. compared to those with bedtime >10:00 p.m. to 11:00 p.m., with estimated ORs of 1.25, 1.27, and 1.16, respectively. Meanwhile, the risk of hearing loss increased by 29% for each 5-risk allele increment of PRS. More importantly, joint analyses showed that the risk of hearing loss was 2-fold in sleep duration ≥9 h/night and high PRS, and 2.18-fold in bedtime ≤9:00 p.m. and high PRS. With significant joint effects of sleep duration and bedtime on hearing loss, we found an interaction of sleep duration with PRS in those with early bedtime and an interaction of bedtime with PRS in those with long sleep duration on hearing loss (P_int <0.05), and such relationships were more evident in high PRS. Similarly, the above relationships were also observed for age-related hearing loss and noise-induced hearing loss, particularly the latter. In addition, age-modified effects of sleep patterns on hearing loss were likewise observed, with stronger estimation among those aged <65 years. Accordingly, longer sleep duration, early bedtime, and high PRS were independently and jointly related to increased risk of hearing loss, suggesting the importance of considering both genetics and sleep pattern for risk assessment of hearing loss.

Genome-Wide Association Study of Metamizole-Induced Agranulocytosis in European Populations

Agranulocytosis is a rare yet severe idiosyncratic adverse drug reaction to metamizole, an analgesic widely used in countries such as Switzerland and Germany. Notably, an underlying mechanism has not yet been fully elucidated and no predictive factors are known to identify at-risk patients. With the aim to identify genetic susceptibility variants to metamizole-induced agranulocytosis (MIA) and neutropenia (MIN), we conducted a retrospective multi-center collaboration including cases and controls from three European populations. Association analyses were performed using genome-wide genotyping data from a Swiss cohort (45 cases, 191 controls) followed by replication in two independent European cohorts (41 cases, 273 controls) and a joint discovery meta-analysis. No genome-wide significant associations (p < 1 × 10⁻⁷) were observed in the Swiss cohort or in the joint meta-analysis, and no candidate genes suggesting an immune-mediated mechanism were identified. In the joint meta-analysis of MIA cases across all cohorts, two candidate loci on chromosome 9 were identified, rs55898176 (OR = 4.01, 95%CI: 2.41–6.68, p = 1.01 × 10⁻⁷) and rs4427239 (OR = 5.47, 95%CI: 2.81–10.65, p = 5.75 × 10⁻⁷), of which the latter is located in the SVEP1 gene previously implicated in hematopoiesis. This first genome-wide association study for MIA identified suggestive associations with biological plausibility that may be used as a stepping-stone for post-GWAS analyses to gain further insight into the mechanism underlying MIA.

High-Throughput Sequencing to Investigate Associations Between HLA Genes and Metamizole-Induced Agranulocytosis

Background and Objective: Agranulocytosis is a rare and potentially life-threatening complication of metamizole (dipyrone) intake that is characterized by a loss of circulating neutrophil granulocytes. While the mechanism underlying this adverse drug reaction is not well understood, involvement of the immune system has been suggested. In addition, associations between genetic variants in the Human Leukocyte Antigen (HLA) region and agranulocytosis induced by other drugs have been reported. The aim of the present study was to assess whether genetic variants in classical HLA genes are associated with the susceptibility to metamizole-induced agranulocytosis (MIA) in a European population by targeted resequencing of eight HLA genes.

Design: A case-control cohort of Swiss patients with a history of neutropenia or agranulocytosis associated with metamizole exposure (n = 53), metamizole-tolerant (n = 39) and unexposed controls (n = 161) was recruited for this study. A high-throughput resequencing (HTS) and high-resolution typing method was used to sequence and analyze eight HLA loci in a discovery subset of this cohort (n = 31 cases, n = 38 controls). Identified candidate alleles were investigated in the full Swiss cohort as well as in two independent cohorts from Germany and Spain using HLA imputation from genome-wide SNP array data. In addition, variant calling based on HTS data was performed in the discovery subset for the class I genes HLA-A, -B, and -C using the HLA-specific mapper hla-mapper.

Results: Eight candidate alleles (p < 0.05) were identified in the discovery subset, of which HLA-C^∗04:01 was associated with MIA in the full Swiss cohort (p < 0.01) restricted to agranulocytosis (ANC < 0.5 × 10⁹/L) cases. However, no candidate allele showed a consistent association in the Swiss, German and Spanish cohorts. Analysis of individual sequence variants in class I genes produced consistent results with HLA typing but did not reveal additional small nucleotide variants associated with MIA.

Conclusion: Our results do not support an HLA-restricted T cell-mediated immune mechanism for MIA. However, we established an efficient high-resolution (three-field) eight-locus HTS HLA resequencing method to interrogate the HLA region and demonstrated the feasibility of its application to pharmacogenetic studies.

Studying the effects of haplotype partitioning methods on the RA-associated genomic results from the North American Rheumatoid Arthritis Consortium (NARAC) dataset

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Haplotype blocks methods plays a complementary role to the single-SNP approaches.

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CIT, FGT, SSLD, and single-SNP methods should be applied to discover the markers.

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Selection of the method used for the association has an impact on the biomarkers.

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SSLD method detected more significant SNPs than CIT, FGT, and single-SNP methods.

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The 383 SNPs discovered by all methods are significantly associated with RA.

The human genome, which includes thousands of genes, represents a big data challenge. Rheumatoid arthritis (RA) is a complex autoimmune disease with a genetic basis. Many single-nucleotide polymorphism (SNP) association methods partition a genome into haplotype blocks. The aim of this genome wide association study (GWAS) was to select the most appropriate haplotype block partitioning method for the North American Rheumatoid Arthritis Consortium (NARAC) dataset. The methods used for the NARAC dataset were the individual SNP approach and the following haplotype block methods: the four-gamete test (FGT), confidence interval test (CIT), and solid spine of linkage disequilibrium (SSLD). The measured parameters that reflect the strength of the association between the biomarker and RA were the P-value after Bonferroni correction and other parameters used to compare the output of each haplotype block method. This work presents a comparison among the individual SNP approach and the three haplotype block methods to select the method that can detect all the significant SNPs when applied alone. The GWAS results from the NARAC dataset obtained with the different methods are presented. The individual SNP, CIT, FGT, and SSLD methods detected 541, 1516, 1551, and 1831 RA-associated SNPs respectively, and the individual SNP, FGT, CIT, and SSLD methods detected 65, 156, 159, and 450 significant SNPs respectively, that were not detected by the other methods. Three hundred eighty-three SNPs were discovered by the haplotype block methods and the individual SNP approach, while 1021 SNPs were discovered by all three haplotype block methods. The 383 SNPs detected by all the methods are promising candidates for studying RA susceptibility. A hybrid technique involving all four methods should be applied to detect the significant SNPs associated with RA in the NARAC dataset, but the SSLD method may be preferred because of its advantages when only one method was used.

A Genome-Wide Association Study Identifies a Candidate Gene Associated With Atazanavir Exposure Measured in Hair

Hair provides a direct measure of long-term exposure of atazanavir (ATV). We report the results of the first genome-wide association study (GWAS) of ATV exposure measured in hair in an observational cohort representative of US women living with HIV; the Women's Interagency HIV Study. Approximately 14.1 million single nucleotide polymorphisms (SNPs) were analyzed in linear regression-based GWAS, with replication, adjusted for nongenetic predictors collected under conditions of actual use of ATV in 398 participants. Lastly, the PharmGKB database was used to identify pharmacogene associations with ATV exposure. The rs73208473, within intron 1 of SORCS2, resulted in a 0.46-fold decrease in ATV exposure, with the strongest association (P = 1.71×10-8 ) in GWAS. A priori pharmacogene screening did not identify additional variants statistically significantly associated with ATV exposure, including those previously published in ATV plasma candidate pharmacogene studies. The findings demonstrate the potential value of pharmacogenomic GWAS in ethnically diverse populations under conditions of actual use.

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data

Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation.