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Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes.

ABSTRACT: More than 1100 genetic loci have been correlated with drug response outcomes but disproportionately few have been translated into clinical practice. One explanation for the low rate of clinical implementation is that the majority of associated variants may be in linkage disequilibrium (LD) with the causal variants, which are often elusive. This study aims to identify and characterize likely causal variants within well-established pharmacogenomic genes using next-generation sequencing data from the 1000 Genomes Project. We identified 69,319 genetic variations within 160 pharmacogenomic genes, of which 8207 variants are in strong LD (r2>0.8) with known pharmacogenomic variants. Of the latter, eight are coding or structural variants predicted to have high impact, with 19 additional missense variants that are predicted to have moderate impact. In conclusion, we identified putatively functional variants within known pharmacogenomics loci that could account for the association signals and represent the missing causative variants underlying drug response phenotypes.

SUBMITTER: Choi J

PROVIDER: S-EPMC6417988 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes.

Choi Jihoon J Tantisira Kelan G KG Duan Qing Ling QL

The pharmacogenomics journal 20180914 2

More than 1100 genetic loci have been correlated with drug response outcomes but disproportionately few have been translated into clinical practice. One explanation for the low rate of clinical implementation is that the majority of associated variants may be in linkage disequilibrium (LD) with the causal variants, which are often elusive. This study aims to identify and characterize likely causal variants within well-established pharmacogenomic genes using next-generation sequencing data from t ...[more]

PMID: 30214008

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Project description:BACKGROUND:Deafness is a highly heterogenous disorder with over 100 genes known to underlie human non-syndromic hearing impairment. However, many more remain undiscovered, particularly those involved in the most common form of deafness: adult-onset progressive hearing loss. Despite several genome-wide association studies of adult hearing status, it remains unclear whether the genetic architecture of this common sensory loss consists of multiple rare variants each with large effect size or many common susceptibility variants each with small to medium effects. As next generation sequencing is now being utilised in clinical diagnosis, our aim was to explore the viability of diagnosing the genetic cause of hearing loss using whole exome sequencing in individual subjects as in a clinical setting. METHODS:We performed exome sequencing of thirty patients selected for distinct phenotypic sub-types from well-characterised cohorts of 1479 people with adult-onset hearing loss. RESULTS:Every individual carried predicted pathogenic variants in at least ten deafness-associated genes; similar findings were obtained from an analysis of the 1000 Genomes Project data unselected for hearing status. We have identified putative causal variants in known deafness genes and several novel candidate genes, including NEDD4 and NEFH that were mutated in multiple individuals. CONCLUSIONS:The high frequency of predicted-pathogenic variants detected in known deafness-associated genes was unexpected and has significant implications for current diagnostic sequencing in deafness. Our findings suggest that in a clinic setting, efforts should be made to a) confirm key sequence results by Sanger sequencing, b) assess segregations of variants and phenotypes within the family if at all possible, and c) use caution in applying current pathogenicity prediction algorithms for diagnostic purposes. We conclude that there may be a high number of pathogenic variants affecting hearing in the ageing population, including many in known deafness-associated genes. Our findings of frequent predicted-pathogenic variants in both our hearing-impaired sample and in the larger 1000 Genomes Project sample unselected for auditory function suggests that the reference population for interpreting variants for this very common disorder should be a population of people with good hearing for their age rather than an unselected population.

Project description:Introduction: Research in the field of pharmacogenomics (PGx) aims to identify genetic variants that modulate response to drugs, through alterations in their pharmacokinetics (PK) or pharmacodynamics (PD). The distribution of PGx variants differs considerably among populations, and whole-genome sequencing (WGS) plays a major role as a comprehensive approach to detect both common and rare variants. This study evaluated the frequency of PGx markers in the context of the Brazilian population, using data from a population-based admixed cohort from Sao Paulo, Brazil, which includes variants from WGS of 1,171 unrelated, elderly individuals. Methods: The Stargazer tool was used to call star alleles and structural variants (SVs) from 38 pharmacogenes. Clinically relevant variants were investigated, and the predicted drug response phenotype was analyzed in combination with the medication record to assess individuals potentially at high-risk of gene-drug interaction. Results: In total, 352 unique star alleles or haplotypes were observed, of which 255 and 199 had a frequency < 0.05 and < 0.01, respectively. For star alleles with frequency > 5% (n = 97), decreased, loss-of-function and unknown function accounted for 13.4%, 8.2% and 27.8% of alleles or haplotypes, respectively. Structural variants (SVs) were identified in 35 genes for at least one individual, and occurred with frequencies >5% for CYP2D6, CYP2A6, GSTM1, and UGT2B17. Overall 98.0% of the individuals carried at least one high risk genotype-predicted phenotype in pharmacogenes with PharmGKB level of evidence 1A for drug interaction. The Electronic Health Record (EHR) Priority Result Notation and the cohort medication registry were combined to assess high-risk gene-drug interactions. In general, 42.0% of the cohort used at least one PharmGKB evidence level 1A drug, and 18.9% of individuals who used PharmGKB evidence level 1A drugs had a genotype-predicted phenotype of high-risk gene-drug interaction. Conclusion: This study described the applicability of next-generation sequencing (NGS) techniques for translating PGx variants into clinically relevant phenotypes on a large scale in the Brazilian population and explores the feasibility of systematic adoption of PGx testing in Brazil.

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Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes.

Publications

Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes.

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