Genotyping, characterization, and imputation of known and novel CYP2A6 structural variants using SNP array data.
Genotyping, characterization, and imputation of known and novel CYP2A6 structural variants using SNP array data.
复制标题
使用 SNP 阵列数据对已知和新型 CYP2A6 结构变体进行基因分型、表征和插补。
DOI:
10.1038/s10038-023-01148-y
复制
发表时间:
2023
影响因子:
3.5
通讯作者:
Tyndale,RachelF
中科院分区:
文献类型:
--
作者:
Langlois,AlecWR;El-Boraie,Ahmed;Pouget,JennieG;Cox,LisaSanderson;Ahluwalia,JasjitS;Fukunaga,Koya;Mushiroda,Taisei;Knight,Jo;Chenoweth,MeghanJ;Tyndale,RachelF
CYP2A6 metabolically inactivates nicotine. Faster CYP2A6 activity is associated with heavier smoking and higher lung cancer risk. TheCYP2A6gene is polymorphic, including functional structural variants (SV) such as gene deletions (CYP2A6*4), duplications (CYP2A6*1×2), and hybrids with theCYP2A7pseudogene (CYP2A6*12,CYP2A6*34). SVs are challenging to genotype due to their complex genetic architecture. Our aims were to develop a reliable protocol for SV genotyping, functionally phenotype known and novel SVs, and investigate the feasibility ofCYP2A6SV imputation from SNP array data in two ancestry populations. European- (EUR;n= 935) and African- (AFR;n= 964) ancestry individuals from smoking cessation trials were genotyped for SNPs using an Illumina array and forCYP2A6SVs using Taqman copy number (CN) assays. SV-specific PCR amplification and Sanger sequencing was used to characterize a novel SV. Individuals with SVs were phenotyped using the nicotine metabolite ratio, a biomarker of CYP2A6 activity. SV diplotype and SNP array data were integrated and phased to generate ancestry-specific SV reference panels. Leave-one-out cross-validation was used to investigate the feasibility ofCYP2A6SV imputation. A minimal protocol requiring three Taqman CN assays forCYP2A6SV genotyping was developed and known SV associations with activity were replicated. The first domain swapCYP2A6-CYP2A7hybrid SV,CYP2A6*53, was identified, sequenced, and associated with lower CYP2A6 activity. In both EURs and AFRs, most SV alleles were identified using imputation (>70% and >60%, respectively); importantly, false positive rates were <1%. These results confirm thatCYP2A6SV imputation can identify most SV alleles, including a novel SV.