The pharmacogenomic landscape in the Chinese: An analytics of pharmacogenetic variants in 206,640 individuals

Abstract

Pharmacogenomic landscapes and related databases are important for identifying the biomarkers of drug response and toxicity. However, these data are still lacking for the Chinese population. In this study, we constructed a pharmacogenomic landscape and an associated database using whole-genome sequencing data generated by non-invasive prenatal testing in 206,640 Chinese individuals. In total, 1,577,513 variants (including 331,610 novel variants) were identified among 3,538 pharmacogenes related to 2,086 drugs. We found that the variant spectrum in the Chinese population differed among the seven major regions. Regional differences also exist among provinces in China. The average numbers of drug enzyme, transporter, and receptor variants were 258, 557, and 632, respectively. Subsequent correlation analysis indicated that the pharmacogenes affecting multiple drugs had fewer variants. Among the 16 categories of drugs, we found that nervous system, cardiovascular system, and genitourinary system/sex hormone drugs were more likely to be affected by variants of pharmacogenes. Characteristics of the variants in the enzyme, transporter, and receptor subfamilies showed specificity. To explore the clinical utility of these data, a genetic association study was conducted on 1,019 lung cancer patients. Two novel variants, AKT2 chr19:40770621 C>G and SLC19A1 chr21:46934171 A>C, were identified as novel platinum response biomarkers. Finally, a pharmacogenomic database, named the Chinese Pharmacogenomic Knowledge Base (CNPKB: http://www.cnpkb.com.cn/), was constructed to collect all the data. In summary, a pharmacogenomic landscape and database for the Chinese population were constructed in this study, which could support personalized Chinese medicine in the future.

Keywords: Chinese; database; next-generation sequence; pharmacogenes; pharmacogenomics; variants.

Graphical abstract

Figure 1

Flowchart and framework of this study The entire study included four major steps: data collection, establishment of the pharmacogenomic landscape, analysis of the landscape, and database construction and clinical application.

Figure 2

The overview of pharmacogene variants in the Chinese population (A) The regional distribution of 206,640 Chinese individuals. (B) The frequency distribution of all variants. (C) The proportion of total and novel variants in exonic, intronic, and intergenic regions. (D) The counts of different types of exonic variants in total and novel variants. (E) The proportions of known and novel potential functional variants. (F) The correlation of variant frequency between the Chinese (CNPKB) and populations of East Asians (EAS), Admixed Americans (AMR), Africans (AFR), Finns (FIN), Non-Finnish Europeans (NFE), and Ashkenazi Jews (ASJ). (G) Disparities in genetic distances, which were calculated by the frequencies of all variants among different Chinese PLADs. PLAD, provincial-level administrative division; MAF, minor allele frequency; UTR, untranslated region.

Figure 3

The landscape of variants in different categories of pharmacogenes (A) Overview of each pharmacogene’s variants and their affected drugs number in the Chinese population. (B) The distribution of variant counts in different pharmacogene categories. Error bars represent the mean ± SD. (C) The top 10 genes in each category of pharmacogenes were more likely to be affected by variants in pharmacogenes. (D) The correlation between gene length and variant counts of pharmacogenes. (E) The distribution of variants’ counts of involved pharmacogenes in different drug categories. Error bars represent the mean ± SD. (F) The top and bottom five drugs in each category according to their summarized pharmacogene variants’ counts. A, alimentary tract; B, blood and blood-forming organs; C, cardiovascular system; D, dermatological; G, genitourinary system and sex hormones; H, systemic hormonal preparations, excluding sex hormones and insulins; J, antiinfectives for systemic use; L, antineoplastic; M, musculoskeletal system; N, nervous system; P, antiparasitic products, insecticides, and repellents; R, respiratory system; S, sensory organs; T, antimetabolic; I, immunomodulating agents; V, various drugs not belonging in the above categories.

Figure 4

The variant pattern and impacted drugs of drug-metabolic enzymes in the Chinese (A) The numbers of variants and affected drugs of each drug enzyme in the Chinese population. (B) Frequency of the star (∗) allele of enzymes in the Chinese population and its comparison with other populations. (C) Correlation of the frequency of functional variants in Chinese populations with Caucasian or African populations. (D) Drugs mostly affected by interethnic different variants. (E) Frequency of drug-metabolic enzyme functional variants in PLADs with sample sizes above 50. (F) Drugs mostly affected by variants that differ among PLADs. (G) The regional differences of six important variants of enzymes in different Chinese PLADs. CHN, Chinese; AFR, African; ASJ, Ashkenazi Jewish; AMR, Admixed American; EAS, East Asian; NFE, Non-Finnish European; FIN, Finnish; PLAD, provincial-level administrative division.

Figure 5

The variant pattern and organic distribution of transporters in the Chinese (A) The numbers of variants and affected drugs of drug transporters in the Chinese population. (B) The distribution of transporters in organs involved in the drug processes of absorption, distribution, and excretion. (C and D) Structure and distribution of variants in different domains of (C) ABCG2 and (D) SLCO1B1. EAS, East Asian; AMR, Admixed American; AFR, African; FIN, Finnish; NFE, Non-Finnish European; ASJ, Ashkenazi Jewish; CHN, Chinese.

Figure 6

The variant pattern and impacted drugs of receptors in the Chinese (A) The numbers of variants and affected drugs of drug receptors in the Chinese population. (B) Variant distribution density in seven transmembrane and other non-transmembrane domains of 87 G-protein-coupled receptors (GPCRs). (C) The connections of detected Chinese exonic functional variants with a frequency >5% in different domains of GPCRs with different categories of drugs. (D) Variant distribution density in four transmembrane domains and other non-transmembrane domains of 31 ligand-gated ion channel receptors (LGICRs). (E) The connections of detected Chinese exonic functional variants with a frequency >5% in different domains of LGICRs with different categories of drugs. (F) Variant distribution density in two binding domains and other domains of 26 nuclear hormone receptor (NHR). (G) The connections of detected Chinese exonic functional variants with a frequency >5% in different domains of NHRs with different categories of drugs. TM, transmembrane; ECL, extracellular loops; ICL, intracellular loops; A, alimentary tract; B, blood and blood-forming organs; C, cardiovascular system; D, dermatological; G, genitourinary system and sex hormones; H, systemic hormonal preparations, excluding sex hormones and insulins; J, antiinfectives for systemic use; L, antineoplastic; M, musculoskeletal system; N, nervous system; P, antiparasitic products, insecticides, and repellents; R, respiratory system; S, sensory organs; T, antimetabolic; I, immunomodulating agents; V, various drugs not belonging to the above categories. The variants with racial differences were marked in red font (C, E and G).

Figure 7

The PGx study of platinum response in lung cancer patients and CNPKB database construction (A) Flowchart of the PGx study design. (B) The proportions of responders and non-responders in lung cancer patients using platinum who carried AKT2 chr19:40770621 C>G or SLC19A1 chr21:46934171 A>C mutations. (C) The potential pathways and mechanisms of variants in AKT2 and SLC19A1 affecting drug sensitivity involved in the platinum resistance process. (D) The structure of the CNPKB database. (E) The QR code for CNPKB, which could be scanned by a mobile phone. (F) The online website presentation of the CNPKB database. SNP, single-nucleotide polymorphism; CNPKB, Chinese Pharmacogenomic Knowledge Base; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; QR code, quick-response code.