Haplotype-specific PCR for NAT2 diplotyping

N-acetyltransferase 2 (NAT2) is an enzyme that acetylates many kinds of drugs, including the antituberculosis drug isoniazid. The NAT2 gene is highly diverse across populations. An individual can be classified as having a slow acetylator (SA), an intermediate acetylator (IA), or a rapid acetylator (RA) phenotype based on its two haplotypes (diplotype) of NAT2. SA individuals are at a higher risk for isoniazid-induced hepatitis, while the RA phenotype contributes to failure in tuberculosis treatment. Being able to predict individual NAT2 phenotypes is important for dose adjustment of isoniazid. NAT2 haplotypes are commonly determined via an indirect method of statistical haplotype inference from SNP genotyping. Here, we report a direct NAT2 haplotyping method using haplotype-specific PCR (HS-PCR) for the 6 most commonly found NAT2 haplotypes: NAT2*4, NAT2*5B, NAT2*6A, NAT2*7B, NAT2*12A, and NAT2*13A. Validation of this HS-PCR method via comparison with a sequencing method in 650 Thai DNA samples (107 RA, 279 IA, and 264 SA samples) showed a concordance rate for diplotype calls of 99.23% (645/650 samples). The discordant results in 5 samples were due to 3 rare NAT2 haplotypes: NAT*5C (n = 3), NAT2*7C (n = 1), and NAT2*11A (n = 1). This novel HS-PCR method allows direct NAT2 diplotyping, enabling the implementation of NAT2 acetylator phenotypes in clinical pharmacogenetic testing.

RAbHIT: R Antibody Haplotype Inference Tool

Summary Antibody haplotype inference (chromosomal phasing) may have clinical implications for the identification of genetic predispositions to diseases. Yet, our knowledge of the genomic loci encoding for the variable regions of the antibody is only partial, mostly due to the challenge of aligning short reads from genome sequencing to these highly repetitive loci. A powerful approach to infer the content of these loci relies on analyzing repertoires of rearranged V(D)J sequences. We present here RAbHIT, an R Haplotype Antibody Inference Tool, that implements a novel algorithm to infer V(D)J haplotypes by adapting a Bayesian framework. RAbHIT offers inference of haplotype and gene deletions. It may be applied to sequences from naïve and non-naïve B-cells, sequenced by different library preparation protocols. Availability and implementation RAbHIT is freely available for academic use from comprehensive R archive network (CRAN) (https://cran.r-project.org/web/packages/rabhit/) under CC BY-SA 4.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

Mosaic deletion patterns of the human antibody heavy chain gene locus as revealed by Bayesian haplotyping

Analysis of antibody repertoires by high-throughput sequencing is of major importance in understanding adaptive immune responses. Our knowledge of variations in the genomic loci encoding antibody genes is incomplete, mostly due to technical difficulties in aligning short reads to these highly repetitive loci. The partial knowledge results in conflicting V-D-J gene assignments between different algorithms, and biased genotype and haplotype inference. Previous studies have shown that haplotypes can be inferred by taking advantage of IGHJ6 heterozygosity, observed in approximately one third of the population. Here, we propose a robust novel method for determining V-D-J haplotypes by adapting a Bayesian framework. Our method extends haplotype inference to IGHD- and IGHV-based analysis, thereby enabling inference of complex genetic events like deletions and copy number variations in the entire population. We generated the largest multi individual data set, to date, of naïve B-cell repertoires, and tested our method on it. We present evidence for allele usage bias, as well as a mosaic, tiled pattern of deleted and present IGHD and IGHV nearby genes, across the population. The inferred haplotypes and deletion patterns may have clinical implications for genetic predispositions to diseases. Our findings greatly expand the knowledge that can be extracted from antibody repertoire sequencing data.