Genetic Risk Factors in Drug-Induced Liver Injury Due to Isoniazid-Containing Antituberculosis Drug Regimens.

Drug-induced liver injury (DILI) is a complication of treatment with antituberculosis (TB) drugs, especially in isoniazid (INH)-containing regimens. To investigate genetic risk factors, we performed a genomewide association study (GWAS) involving anti-TB DILI cases (55 Indian and 70 European) and controls (1,199 Indian and 10,397 European). Most cases were treated with a standard anti-TB drug regimen; all received INH. We imputed single nucleotide polymorphism and HLA genotypes and performed trans-ethnic meta-analysis on GWAS and candidate gene genotypes. GWAS found one significant association (rs117491755) in Europeans only. For HLA, HLA-B*52:01 was significant (meta-analysis odds ratio (OR) 2.67, 95% confidence interval (CI) 1.63-4.37, P = 9.4 × 10-5 ). For N-acetyltransferase 2 (NAT2), NAT2*5 frequency was lower in cases (OR 0.69, 95% CI 0.57-0.83, P = 0.01). NAT2*6 and NAT2*7 were more common, with homozygotes for NAT2*6 and/or NAT2*7 enriched among cases (OR 1.89, 95% CI 0.84-4.22, P = 0.004). We conclude HLA genotype makes a small contribution to TB drug-related DILI and that the NAT2 contribution is complex, but consistent with previous reports when differences in the metabolic effect of NAT2*5 compared with those of NAT2*6 and NAT2*7 are considered.

MetaSeq: privacy preserving meta-analysis of sequencing-based association studies.

Human genetics recently transitioned from GWAS to studies based on NGS data. For GWAS, small effects dictated large sample sizes, typically made possible through meta-analysis by exchanging summary statistics across consortia. NGS studies groupwise-test for association of multiple potentially-causal alleles along each gene. They are subject to similar power constraints and therefore likely to resort to meta-analysis as well. The problem arises when considering privacy of the genetic information during the data-exchange process. Many scoring schemes for NGS association rely on the frequency of each variant thus requiring the exchange of identity of the sequenced variant. As such variants are often rare, potentially revealing the identity of their carriers and jeopardizing privacy. We have thus developed MetaSeq, a protocol for meta-analysis of genome-wide sequencing data by multiple collaborating parties, scoring association for rare variants pooled per gene across all parties. We tackle the challenge of tallying frequency counts of rare, sequenced alleles, for metaanalysis of sequencing data without disclosing the allele identity and counts, thereby protecting sample identity. This apparent paradoxical exchange of information is achieved through cryptographic means. The key idea is that parties encrypt identity of genes and variants. When they transfer information about frequency counts in cases and controls, the exchanged data does not convey the identity of a mutation and therefore does not expose carrier identity. The exchange relies on a 3rd party, trusted to follow the protocol although not trusted to learn about the raw data. We show applicability of this method to publicly available exome-sequencing data from multiple studies, simulating phenotypic information for powerful meta-analysis. The MetaSeq software is publicly available as open source.