The SH-SY5Y Pharmacogenome Resolved by Long-Read Whole-Genome Sequencing

The SH-SY5Y Pharmacogenome Resolved by Long-Read Whole-Genome Sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The SH-SY5Y Pharmacogenome Resolved by Long-Read Whole-Genome Sequencing Wojciech Kuban, Paula Konowalska, Malgorzata Borczyk, Marcin Piechota, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9224585/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract SH-SY5Y neuroblastoma cells (SH-SY5Y) are widely used for mechanistic neuropharmacology and toxicity studies, yet their pharmacogenomic background is not explicitly specified. We generated ~ 30× PacBio HiFi whole-genome data using the WOBI workflow (Revio), performed QC with FastQC, aligned reads to GRCh38 (pbmm2), and called SNVs/indels (GATK), SVs (pbsv), and CNVs (HiFiCNV). Pharmacogene star-allele diplotypes were assigned using Polygenic with PharmVar-based definitions, complemented by VEP and gnom AD annotations. The selected genes coding for drug-metabolizing enzymes or transporters ( CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, CYP3A5, ABCB1) , and encoding neurotransmitter receptors, metabolism or transport (DRD2, HTR2A, HTR2C, COMT, SLC6A4) were investigated. SH-SY5Y carried CYP2C19*17/*17 (ultrarapid metabolism), CYP3A5*3/*3 (non-expresser), CYP2A6*9/*1 (intermediate metabolism), and CYP1A2*1F with increased CYP1A2 inducibility. Furthermore, a diversity of non-coding regions was found in the studied loci, which may influence gene expression. This long-read reference provides an actionable baseline for designing and interpreting pharmacological experiments with SH-SY5Y. Biological sciences/Computational biology and bioinformatics/Gene regulatory networks Biological sciences/Biotechnology/Sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Neuropsychiatric disorders remain difficult to model mechanistically and to treat effectively, in part because of drug response and adverse effects vary widely across individuals. In vitro systems, especially human neuronal cell lines, support the bridge from discovery to translational pharmacology. The SH-SY5Y neuroblastoma cell line is widely used due to its human origin, capacity for neuronal differentiation, and ability to reproduce selected aspects of central nervous system pharmacology ( 1 ). SH-SY5Y cells (SH-SY5Y) express multiple neurotransmitter receptors and transporters and can be directed toward dopaminergic or cholinergic phenotypes, enabling applications in target validation, drug screening, mechanistic studies, and toxicity testing ( 2 ). Despite extensive use, SH-SY5Y has not been systematically profiled for genetic variation in genes with direct pharmacogenomic relevance. This gap limits interpretability and cross-study comparability, particularly when experiments involve drugs whose disposition and pharmacodynamics are strongly genotype-dependent. Cytochrome P450 (CYP) enzymes are central determinants of interindividual variability in psychopharmacological treatment and constitute the largest phase I drug-metabolizing enzyme family ( 3 ). Key pharmacogenes relevant to psychopharmacology include hepatic and extrahepatic CYPs as well as major neuropsychiatric targets and transport pathways. CYP1A2 contributes substantially to hepatic CYP content and metabolizes caffeine, clozapine, and other psychoactive agents; its activity is modulated by environmental exposures (e.g., smoking) and transcriptional/epigenetic regulation ( 4 ). CYP2A6 influences nicotine clearance with clinical implications for smoking behavior and cessation outcomes ( 5 ). CYP2B6 metabolizes efavirenz and methadone and shows marked interindividual variability driven by polymorphism and alternative splicing ( 6 ). The CYP2C cluster (10q24) includes CYP2C8 and CYP2C9, which contribute to clearance of multiple therapeutic drug classes and are regulated by nuclear receptors such as pregnane X receptor and constitutive androstane receptor (PXR and CAR, respectively) ( 7 ), while CYP2C19 is a clinically prominent pharmacogene affecting clopidogrel, proton pump inhibitors, antidepressants and antipsychotics, with established consequences of loss- and gain-of-function alleles ( 8 – 10 ). CYP2D6 metabolizes approximately a quarter of prescribed drugs, including many antidepressants and antipsychotics; its complex genetic architecture (including copy-number variation) drives the continuum from poor to ultrarapid metabolizer phenotypes, and brain expression may further contribute to neurochemical effects ( 11 ). Moreover, CYP2C19 and CYP2D6 take part in the metabolism of endogenous substrates including steroid hormones, monoaminergic neurotransmitters and endocannabinoids, affecting in this way brain development and function ( 12 , 13 ). CYP2E1 links ethanol/metabolic stress to xenobiotic metabolism and oxidative stress pathways ( 14 ), whereas CYP3A4 and CYP3A5 account for a large fraction of overall drug metabolism, with CYP3A5 expression varying substantially across individuals due to splice-site polymorphisms that impact dosing of drugs such as tacrolimus ( 15 ). Beyond drug metabolism, variation in catecholamine O- methyltranspherase (COMT) modulates catecholamine neurotransmitter inactivation with established links to prefrontal cortical function and complex genetic associations with schizophrenia ( 16 ). DRD2 encodes the dopamine D 2 receptor, a principal mediator of antipsychotic efficacy and a key regulator of striatal signaling and reward-related behaviors ( 17 ). SLC6A4 encodes the serotonin transporter (SERT/5-HTT), the primary molecular target of selective serotonin reuptake inhibitors − SSRIs (and an important target for many serotonin noradrenaline reuptake inhibitors − SNRIs).Promoter variation at 5-HTTLPR is associated with altered transcription and transporter expression ( 18 ). HTR2A and HTR2C encode the 5-HT 2A and 5-HT 2C receptors, respectively; these receptors represent major pharmacological nodes in cortical and appetite/energy-balance circuits, with HTR2C additionally shaped by extensive RNA editing and alternative splicing, and repeatedly evaluated in the context of antipsychotic-induced weight gain ( 19 ). The abovementioned genes (Fig. 1 ) define a core axis for psychopharmacogenomics, spanning drug metabolism, transport, and central pharmacodynamic targets. Here, we aim to refine the utility of SH-SY5Y as an in vitro model for psycho- and neuropharmacological research by systematically sequencing and annotating variants in these pharmacogenes. This framework is intended to improve interpretability of SH-SY5Y-based experiments, support more reproducible inference about drug disposition and response, and facilitate downstream precision-oriented neuropsychopharmacological research. Materials and Methods Cell line and genomic DNA isolation SH-SY5Y human neuroblastoma cells were obtained from ATCC (CRL-2266; lot: 70047955) and harvested at ~ 80% confluence. High-molecular-weight (HMW) genomic DNA was isolated from cell pellets using the Monarch® HMW DNA Extraction Kit (New England Biolabs) following the manufacturer’s protocol, with gentle handling to minimize DNA shearing. DNA was eluted in the supplied buffer and stored at 4°C until library preparation. DNA concentration was measured by fluorometry (Qubit dsDNA Assay). Purity was assessed by UV spectrophotometry (A260/A280 and A260/A230). HMW DNA integrity and size distribution were verified by pulsed-field gel electrophoresis (or equivalent capillary electrophoresis). Only preparations showing predominantly HMW material with minimal smearing and no evidence of protein/RNA contamination were advanced to library construction. PacBio WOBI whole-genome sequencing Whole-genome long-read sequencing was performed using the PacBio Human Whole-Genome Sequencing (WOBI) workflow. SMRTbell® libraries were prepared from HMW DNA according to the manufacturer’s recommendations and sequenced on a PacBio long-read platform (Revio system) to a target depth of ~ 30×. Circular consensus sequencing (CCS) processing was used to generate HiFi reads. Sequencing output was subjected to standard quality control (QC). Per-read accuracy, read length, and yield metrics were evaluated using PacBio tools (Figure S1 ). Only HiFi reads meeting predefined QC thresholds were used for downstream analyses. Long-read mapping and variant calling Long-read data quality was assessed using FastQC (v0.12.1; configured for very long reads) ( 20 ). The dataset showed a median quality score of 39 and a median read length of ~ 14,000 bp (Figure S2 ). HiFi reads were aligned to GRCh38 using pbmm2, with default parameters ( 21 ). SNVs and small indels were called using GATK HaplotypeCaller (v4.6.0.0) ( 22 ). Structural variants (SVs) were called using PBSV (v2.9.0) ( 23 ), and copy-number variants (CNVs) were inferred using HiFiCNV (v1.0.1) ( 24 ). CNVs spanning target pharmacogenes (CYP1A2–CYP3A5, COMT, DRD2, SLC6A4, HTR2A, HTR2C, ABCB1) were evaluated by intersecting HiFiCNV and PBSV outputs with gene-level genomic intervals and retaining concordant or otherwise supported calls. Variant annotation Pharmacogenetic star-allele calling was performed using Polygenic ( 25 ), starting from VCF files for the target pharmacogenes. Polygenic assigns star alleles per gene and reports the corresponding predicted phenotype based on Pharmacogene Variation Consortium (PharmVar) definitions ( 26 ). SNVs and small indels were additionally annotated using the Variant Effect Predictor (VEP v113.0) ( 27 ). VEP annotation was restricted to genes listed in Supplementary Table 1 (Table S1 ) plus ± 5 kbp upstream and downstream of each gene boundary. Population allele-frequency annotations were added from gnomAD (v4.1.0) ( 28 ). Read-level evidence was inspected using the Integrative Genomics Viewer (IGV v2.17.4) ( 29 ). Supplementary Fig. 3 (Figure S3 ) shows the representative IGV view of CYP3A5 rs776746 (CYP3A5*3) in SH-SY5Y HiFi WGS data. Results CYP1A2 Four variants were identified within the CYP1A2 locus (15q24.1.) in SH-SY5Y (Table 1): two intronic variants (rs762551, rs2472304), one synonymous coding variant (rs2470890), and one 3′ UTR variant (rs33923017). The variant rs762551 (CYP1A2*1F) is a well-characterized allele associated with increased CYP1A2 inducibility in the presence of environmental inducers (e.g., cigarette smoke), with downstream effects on drug metabolism ( 30 ). In SH-SY5Y, the observed allele fraction for rs762551 (AF = 0.63) was consistent with the corresponding gnomAD allele frequency, supporting population-relevant representation of this locus ( 31 ). CYP2A6 Across the CYP2A6 locus (19q13.2.), 25 SNPs were detected in SH-SY5Y (predominantly upstream/downstream and intronic/non-coding transcript contexts; one synonymous variant was observed; no protein-altering coding variants were identified) (Fig. 2 ). Star-allele calling resolved CYP2A6 as the *9/*1 diplotype, comprising one reduced-function allele (*9) and one reference allele (*1). The CYP2A6*9 allele is defined by the promoter SNP rs28399433 (− 48T > G, TATA box), which has been associated with decreased transcription and reduced enzyme expression ( 35 ). Consistent with curated genotype-to-phenotype mappings, *9/*1 corresponds to an intermediate (reduced) metabolizer status ( 36 ). Functionally, reduced CYP2A6 activity is expected to decrease clearance of nicotine, coumarin, tegafur, and tobacco-derived nitrosamines ( 36 , 37 ). CYP2B6 Across the CYP2B6 locus (19q13.2.), 73 SNPs were detected in SH-SY5Y, with annotations dominated by intronic / upstream–downstream regulatory categories and several 3′ UTR / non-coding transcript features; no protein-altering coding variants were observed in the called set. Star-allele calling resolved SH-SY5Y as CYP2B6 *1/*1, consistent with two reference alleles and a predicted normal (extensive) metabolizer phenotype ( 38 ). CYP2C8 Across the CYP2C8 locus (10q23.33.), 85 SNPs were detected in SH-SY5Y, with annotations dominated by intronic/non-coding transcript (including NMD-transcript) features and upstream/downstream regulatory context, plus a small subset mapping to the 3′ UTR ( 39 , 40 ). Star-allele calling resolved CYP2C8 as *1/*1, consistent with normal enzymatic function ( 26 ). Because CYP2C8 resides within the CYP2C gene cluster, functionally relevant alleles can occur on shared haplotypes with neighboring genes (notably CYP2C9), which is relevant when interpreting multi-gene pharmacogenomic backgrounds ( 41 ). CYP2C9 Across the CYP2C9 locus (10q23.33.), 72 SNPs were detected in SH-SY5Y, with annotations dominated by intronic / non-coding transcript (including NMD-transcript) features and upstream/downstream regulatory context, plus several 3′ UTR variants. Star-allele calling resolved CYP2C9 as *1/*1, corresponding to two reference alleles. Consistent with PharmVar/clinical curation, *1/*1 supports a normal metabolizer phenotype. Because CYP2C9 resides within the CYP2C gene cluster, linkage disequilibrium with neighboring loci can be relevant for multi-gene interpretation (for example, CYP2C9*2 may co-occur with CYP2C8*3 in some populations), which is explicitly tracked in CPIC supplemental materials for CYP2C9-guided NSAID use ( 41 , 42 ). CYP2C19 Across the CYP2C19 locus (10q24.12.), 84 SNPs were detected in SH-SY5Y (Fig. 3 ). The call set was dominated by intronic/non-coding transcript annotations with additional upstream/downstream variants; a single synonymous variant (rs17885098) and one missense-annotated site (rs3758581) were present in the VEP output, without altering the star-allele assignment. Star-allele calling resolved CYP2C19 as *17/*17, a gain-of-function genotype. The *17 allele is defined by the promoter variant rs12248560 (− 806C > T) and has been shown to increase promoter activity, resulting in higher transcription and enzyme expression ( 43 ). Consistent with established genotype-to-phenotype translation, the CYP2C19*17/*17 genotype corresponds to an ultrarapid metabolizer phenotype, which is expected to increase clearance (and lower exposure) for CYP2C19 substrates, including proton pump inhibitors, diazepam, and several antidepressants ( 44 ). CYP2D6 Within the CYP2D6 locus (22q13.2.), no SNPs/short variants passing QC were detected in the SH-SY5Y call set. High-coverage sequencing confirmed the CYP2D6 *1/*1 diplotype, consistent with prior copy-number characterization of SH-SY5Y ( 45 ). This diplotype is consistently translated as a normal metabolizer phenotype and is expected to encode a fully functional enzyme ( 46 ). Consequently, SH-SY5Y provides a genetically baseline CYP2D6 background, without decreased activity due to common no-/reduced-function alleles or increased activity driven by gene duplications or multiplications. CYP2E1 Across the CYP2E1 locus (10q26.3.), 63 variants were detected in SH-SY5Y. The call set was dominated by intronic variants and additional upstream/downstream sites; multiple entries were annotated as non-coding transcript–related features. A single 5′ UTR indel was observed (rs11445593), and two 3′ UTR variants were detected (rs2480256, rs2480257). No coding loss-of-function variants were identified, and no missense changes were detected among the curated variants listed here. The CYP2E1 profile in SH-SY5Y cells is most consistent with intact catalytic capacity for canonical substrates (ethanol, acetone, benzene, nitrosamines), while non-coding variation provides plausible routes for more subtle differences in basal expression and inducibility, which should be considered when CYP2E1-dependent endpoints are interpreted ( 47 ). CYP3A4 Across the CYP3A4 locus (7q22.1.), 21 variants were detected in SH-SY5Y. These were predominantly intronic and upstream/downstream variants, with one 5′ UTR variant (rs1477357584) and no coding loss-of-function changes in the sites listed here. Star-allele calling resolved CYP3A4 as *1/*1, consistent with two reference alleles and a normal metabolizer background ( 48 ). CYP3A5 Across the CYP3A5 locus (7q22.1.), 5 variants were detected in SH-SY5Y (predominantly non-coding context, including one 5′ UTR variant [rs28371764], two 3′ UTR variants [rs4646450, rs80148964], and regulatory/intronic annotations). Star-allele calling resolved CYP3A5 as *3/*3, i.e., two no-function alleles (Fig. 4 ). The defining *3 variant rs776746 (6986A > G) creates an aberrant splice acceptor site that disrupts splicing and effectively abolishes CYP3A5 protein expression, corresponding to a CYP3A5 non-expresser / poor metabolizer phenotype ( 15 , 49 ). Functionally, this genotype predicts minimal to absent CYP3A5 contribution to CYP3A-dependent metabolism in SH-SY5Y, resulting in CYP3A4 predominance within the CYP3A cluster for relevant substrates, such as tacrolimus ( 50 ). DRD2 Across DRD2 (11q23.2.), we observed a dense set of non-coding variants spanning upstream/downstream regions, introns, and both UTRs (83 SNPs). The protein-coding sequence appears intact, as no missense or loss-of-function variants were detected. Two synonymous coding variants were present (rs6277/C957T and rs6275). Although synonymous, both have been repeatedly interrogated as haplotype anchors associated with inter-individual differences in DRD2 expression/availability and neurobehavioral phenotypes, often with context-dependent effects across cohorts and exposures ( 51 ). These data support a DRD2 locus in SH-SY5Y in which sequence-disrupting variation is absent, while expression and RNA handling may vary via UTR and promoter-proximal context, with synonymous anchors (notably rs6277) serving as tractable haplotype markers for downstream interpretation ( 51 ). COMT Variant calling across the COMT locus (22q11.21) revealed a dense, predominantly non-coding variant landscape. Most sites mapped to intronic and flanking upstream/downstream regions, with multiple UTR positions and frequent consequence annotations on alternative COMT transcripts (including isoforms predicted to undergo NMD). Coding variation was limited to two canonical sites: the synonymous rs4633 (C > T) and the missense rs4680 (Val158Met; G > A); no additional missense changes and no predicted coding loss-of-function variants were observed. The presence of rs4680 (Val158Met) is consistent with the well-described functional COMT axis, where the Met (A) allele is associated with reduced enzyme thermostability and lower COMT activity ( 52 , 53 ). Functional analyses of COMT genetic variation in human brain tissue support genotype-dependent differences in mRNA, protein abundance, and enzymatic activity, providing biological plausibility for activity shifts in rs4680-defined backgrounds ( 52 ). In addition, several 3′-UTR variants were present (including rs165599, rs165728, rs165737, rs165774, rs165895), consistent with a haplotype-rich post-transcriptional regulatory region that can mark expression-modulating LD backgrounds. SLC6A4 Variant calls across SLC6A4 (17q11.2) were dominated by non-coding polymorphisms, with most sites mapping to intronic sequence (e.g., the rs2020936/rs2020937/rs2020938 cluster) and a smaller set of UTR-adjacent variants. Several of these markers have been used as linkage disequilibrium (LD) tags across the locus in diverse cohorts ( 54 – 57 ). In our call set, rs2066713 (intronic) and rs8071667 (intronic) represent commonly studied variants that have been examined in relation to substance-use and mood-related phenotypes ( 58 ), while a single 3′-UTR variant (chr17:30,197,405 G > T) highlights the potential for post-transcriptional regulation via 3′-UTR–binding proteins ( 59 ). We also observed one 5′-UTR/promoter-proximal SNP (rs6354) ( 60 ). Multiple entries were annotated to non-coding/NMD-associated transcript models (e.g., rs4636964, rs8071583, rs55817931), consistent with the complex isoform architecture reported for this locus and the long-standing evidence that regulatory variation can influence SLC6A4 expression and treatment-related outcomes. No protein-coding missense or predicted loss-of-function variants were detected, suggesting an intact transporter coding sequence in this dataset. Accordingly, any functional impact if present in this cell-line context would be expected to arise primarily from regulatory variants (5′/3′ UTR, promoter-proximal) and/or intronic haplotype structure, rather than altered protein sequence. HTR2A Variant calls across HTR2A (13q14.2) showed a dense predominance of non-coding polymorphisms, with most sites annotated as intronic or flanking (upstream/downstream) variants. Several variants mapped to the 3′-UTR (rs7324017, rs3803189, rs3125) ( 61 , 62 ), and we also observed promoter/5′-UTR–proximal variants (rs6310, rs6312), consistent with prior evidence that regulatory variation at this locus can modulate cortical HTR2A expression and has been repeatedly examined in treatment-response contexts ( 61 , 63 ). The only coding-region change in this call set was rs6313 (T102C), a synonymous variant that has been evaluated across multiple neuropsychiatric and treatment-related phenotypes (e.g., pain traits, autism-related measures, and antipsychotic response), with results that are often cohort- and ancestry-dependent ( 64 , 65 ). In addition, rs7997012, a commonly studied intronic pharmacogenetic marker in antidepressant response was present ( 66 ). No missense or predicted loss-of-function variants were detected, consistent with an intact HTR2A coding sequence; any functional signal is therefore most likely regulatory and haplotype-driven rather than protein-altering. HTR2C Whole-genome variant calls across HTR2C (Xq23) reveal an extremely dense landscape of non-coding variation, dominated by intronic and upstream/downstream annotations. Among transcript-proximal sites, we observed a single 5′-UTR/promoter-proximal variant (rs518147; −697G/C). Within the protein-coding sequence, only one amino-acid–altering variant was detected: rs6318 (Cys23Ser), a commonly studied missense polymorphism in HTR2C ( 67 ). No additional missense or predicted loss-of-function variants were observed, consistent with preserved coding integrity in the SNV/short-indel call set; any functional effect is therefore more likely regulatory/haplotype- and transcript-context driven than due to coding disruption. ABCB1 The ABCB1 callset is consistent with a locus (7q21.12.) where observable WGS SNV diversity is largely regulatory/haplotype-tagging, rather than protein-disrupting. Alongside extensive intronic/flanking variation, we observe two 3′-UTR variants (rs3842, rs17064) ( 68 ) and one 5′-UTR variant (rs2214102) ( 69 ), any of which could plausibly influence expression through post-transcriptional or promoter-proximal mechanisms, depending on haplotype background and tissue context. In coding regions, we identify the classic pharmacogenetic markers rs1128503 (1236C > T) and rs1045642 (3435C > T) ( 70 ) plus the triallelic missense rs2032582 (2677G > T/A) ( 71 ). The presence of this trio supports haplotype-based analyses (rather than single-SNP inference), because reported effects on P-gp expression/transport are often modest, substrate-dependent, and tissue-specific, and are frequently interpreted through the linked 1236–2677–3435 haplotype structure ( 72 ). In aggregate, the coding sequence appears structurally preserved (no LoF calls), and any functional signal in this dataset would most plausibly arise from haplotype context and regulatory variation rather than coding disruption ( 72 ). Discussion We generated a high-coverage, long-read genome profile for SH-SY5Y that resolves key pharmacogene haplotypes. Because SH-SY5Y is widely used to model neuronal drug response and toxicity, yet its pharmacogenomic background is often incompletely specified ( 1 , 2 ), these data provide a baseline for interpreting metabolism-, receptor- and transporter-dependent readouts. Using PacBio HiFi with pharmacogene interpretation (e.g., PharmVar/PharmGKB resources) ( 26 ), we define star-allele diplotypes across major CYP families, summarize receptor and transporter variations, and highlight loci where non-coding diversity may affect gene expression without implying coding disruption. CYP3A4 and CYP3A5 contribute to the metabolism of many clinically used drugs including psychotropics, and endogenous steroids ( 12 , 73 ). SH-SY5Y carries CYP3A4*1/*1 (consistent with a “normal” CYP3A4 genotype) and CYP3A5*3/*3 , a non-expresser configuration that minimizes CYP3A5 contribution ( 15 , 50 ). This predicts CYP3A4-dominant CYP3A metabolism in SH-SY5Y and simplifies CYP3A4-focused functional tests (e.g., inhibitor/inducer studies)( 48 , 50 , 73 ). CYP2C19*17/*17 predicts an ultrarapid metabolism background for CYP2C19 substrates including psychotropics and estrogen steroids ( 10 , 13 ), and is clinically actionable in CPIC contexts ( 9 ). In cell-line pharmacology, a practical consequence is that CYP2C19-cleared compounds may show faster turnover than a “normal” CYP2C19 background and lower parent drug exposure ( 8 – 11 , 74 ). In contrast, CYP2D6*1/*1 (single copy) supports a baseline “normal” CYP2D6 genotype without duplication-driven ultrarapid metabolism ( 11 , 74 – 76 ). This is particularly relevant because CYP2D6 contributes to the metabolism of many psychotropic drugs and endogenous neuroactive substrates ( 12 , 77 ) and supports cleaner probe-substrate and inhibitor study designs ( 11 , 74 ). At CYP2E1 , we observe extensive intronic/flanking and other non-coding annotations, but no additional missense or predicted loss-of-function variants in the provided call list, consistent with preservation of the protein-coding sequence captured by SNV/short-indel calls. This pattern supports a model where expression/inducibility (rather than catalytic sequence disruption) is the more plausible axis of variability relevant to experiments coupling xenobiotic metabolism with oxidative stress phenotypes ( 14 , 47 ). Furthermore, the variant rs762551 (CYP1A2*1F) found in SH-SY5Y is a well-characterized allele associated with increased CYP1A2 inducibility in the presence of environmental inducers (e.g., cigarette smoke), with downstream effects on drug biotransformation ( 30 , 31 , 78 ), endogenous metabolism of sex steroids and melatonin ( 77 ). The configuration of ABCB1 in SH-SY5Y does not imply transporter loss of function, but it supports haplotype-aware interpretation in assays where intracellular exposure affects readouts ( 71 , 72 , 79 ). Across DRD2, HTR2A, HTR2C, COMT , and SLC6A4 , no coding loss-of-function alleles were detected; any effects in this model are therefore more likely regulatory and will require functional follow-up. Considering the implications for using the obtained SH-SY5Y pharmacogenomic data in “in vitro” experiments, it should be emphasized that the present work provides a genotype-defined drug disposition context ( CYP2C19*17/*17; CYP3A4 -dominant CYP3A due to CYP3A5*3/*3; CYP2D6 *1/*1, single copy). Thus, CYP2C19-cleared probes may show reduced parent drug exposure and CYP3A studies should be planned around CYP3A4. Because ABCB1 carries a common 1236–2677–3435 haplotype block rather than loss-of-function, and because CNV/SV can bias apparent zygosity in cell lines, intracellular exposure and genotype calls should be interpreted haplotype- and CNV/SV-aware. In conclusion, SH-SY5Y provides a comparatively “interpretable” pharmacogenomic baseline CYP3A4-dominant CYP3A, normal CYP2D6, and ultrarapid CYP2C19 with transporter and regulatory context (not coding LoF) as the main expected modifiers of intracellular drug exposure and response. Long-read sequencing reduces ambiguity in pharmacogenes with paralogy/repeats and improves star-allele and phase resolution, making the resulting SH-SY5Y genotype directly usable for probe selection and drug exposure interpretation ( 26 , 80 ). However, functional confirmation (CYP isoform-resolved RNA, protein abundance, and probe phenotyping) is required to translate diplotypes and non-coding variation into calibrated activity priors, particularly for loci where regulation/induction may dominate over coding changes ( 3 , 47 ). Declarations Funding statement This work was supported by Grant OPUS 23 no 2022/45/B/NZ7/02419 from the National Science Centre, Kraków, Poland Conflict of interest The authors declare no conflict of interest. Authorship contributions W. Kuban designed the study, acquired data, prepared DNA samples, interpreted the results, and drafted the manuscript; P. Konowalska acquired data and revised the manuscript; M. Borczyk acquired data and revised the manuscript; M. Piechota acquired data and revised the manuscript; M. Korostyński interpreted the results and revised the manuscript; W. A. Daniel designed the study, interpreted the results, and revised the manuscript. All authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work. Data availability PacBio HiFi whole-genome sequencing data generated for SH-SY5Y in this study have been submitted to the NCBI Sequence Read Archive (SRA). References H. Xicoy, B. Wieringa, G. J. M. Martens, The SH-SY5Y cell line in Parkinson’s disease research: a systematic review. Mol. Neurodegener. 12, 10 (2017). J. Kovalevich, D. Langford, Considerations for the Use of SH-SY5Y Neuroblastoma Cells in Neurobiology. Methods Mol. Biol. Clifton NJ 1078, 9–21 (2013). U. M. Zanger, M. Schwab, Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther. 138, 103–141 (2013). S.-F. Zhou, B. Wang, L.-P. Yang, J.-P. Liu, Structure, function, regulation and polymorphism and the clinical significance of human cytochrome P450 1A2. Drug Metab. Rev. 42, 268–354 (2010). J.-A. Tanner, B. Prasad, K. G. Claw, P. Stapleton, A. Chaudhry, E. G. Schuetz, K. E. Thummel, R. F. Tyndale, Predictors of Variation in CYP2A6 mRNA, Protein, and Enzyme Activity in a Human Liver Bank: Influence of Genetic and Nongenetic Factors. J. Pharmacol. Exp. Ther. 360, 129–139 (2017). U. M. Zanger, K. Klein, Pharmacogenetics of cytochrome P450 2B6 (CYP2B6): advances on polymorphisms, mechanisms, and clinical relevance. Front. Genet. 4 (2013). J. Kirchheiner, J. Brockmöller, Clinical consequences of cytochrome P450 2C9 polymorphisms. Clin. Pharmacol. Ther. 77, 1–16 (2005). J. L. Mega, S. L. Close, S. D. Wiviott, L. Shen, R. D. Hockett, J. T. Brandt, J. R. Walker, E. M. Antman, W. Macias, E. Braunwald, M. S. Sabatine, Cytochrome p-450 polymorphisms and response to clopidogrel. N. Engl. J. Med. 360, 354–362 (2009). C. R. Lee, J. A. Luzum, K. Sangkuhl, R. S. Gammal, M. S. Sabatine, C. M. Stein, D. F. Kisor, N. A. Limdi, Y. M. Lee, S. A. Scott, J.-S. Hulot, D. M. Roden, A. Gaedigk, K. E. Caudle, T. E. Klein, J. A. Johnson, A. R. Shuldiner, Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update. Clin. Pharmacol. Ther. 112, 959–967 (2022). S. C. Sim, C. Risinger, M.-L. Dahl, E. Aklillu, M. Christensen, L. Bertilsson, M. Ingelman-Sundberg, A common novel CYP2C19 gene variant causes ultrarapid drug metabolism relevant for the drug response to proton pump inhibitors and antidepressants. Clin. Pharmacol. Ther. 79, 103–113 (2006). S. Miksys, R. F. Tyndale, The Unique Regulation of Brain Cytochrome P450 2 (CYP2) Family Enzymes by Drugs and Genetics. Drug Metab. Rev. 36, 313–333 (2004). A. Haduch, W. A. Daniel, The engagement of brain cytochrome P450 in the metabolism of endogenous neuroactive substrates: a possible role in mental disorders. Drug Metab. Rev. 50, 415–429 (2018). J. C. Stingl, C. Scholl, J. E. Bosch, R. Viviani, Genetic polymorphism of CYP2C19 and subcortical variability in the human adult brain. Transl. Psychiatry 11, 467 (2021). F. P. Guengerich, Common and Uncommon Cytochrome P450 Reactions Related to Metabolism and Chemical Toxicity. Chem. Res. Toxicol. 14, 611–650 (2001). P. Kuehl, J. Zhang, Y. Lin, J. Lamba, M. Assem, J. Schuetz, P. B. Watkins, A. Daly, S. A. Wrighton, S. D. Hall, P. Maurel, M. Relling, C. Brimer, K. Yasuda, R. Venkataramanan, S. Strom, K. Thummel, M. S. Boguski, E. Schuetz, Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat. Genet. 27, 383–391 (2001). E. M. Tunbridge, P. J. Harrison, D. R. Weinberger, Catechol-o-methyltransferase, cognition, and psychosis: Val158Met and beyond. Biol. Psychiatry 60, 141–151 (2006). J.-M. Beaulieu, R. R. Gainetdinov, The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol. Rev. 63, 182–217 (2011). D. L. Murphy, P. R. Moya, Human Serotonin Transporter Gene (SLC6A4) Variants: Their Contributions to Understanding Pharmacogenomic and Other Functional G x G and G x E Differences in Health and Disease. Curr. Opin. Pharmacol. 11, 3–10 (2011). B. R. Cummins, G. B. Billac, D. E. Nichols, C. D. Nichols, 5-HT2A receptors: Pharmacology and functional selectivity. Pharmacol. Rev. 77, 100059 (2025). Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ . PacificBiosciences/pbmm2, PacBio (2026); https://github.com/PacificBiosciences/pbmm2 . A. McKenna, M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis, A. Kernytsky, K. Garimella, D. Altshuler, S. Gabriel, M. Daly, M. A. DePristo, The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010). PacificBiosciences/pbsv (2025). https://github.com/PacificBiosciences/pbsv . PacificBiosciences/HiFiCNV (2025). https://github.com/PacificBiosciences/HiFiCNV . F. Ruzicka, Polygenic SA selection in the UK Biobank. doi: 10.5281/zenodo.6824671 (2022). A. Gaedigk, S. T. Casey, M. Whirl-Carrillo, N. A. Miller, T. E. Klein, Pharmacogene Variation Consortium: A Global Resource and Repository for Pharmacogene Variation. Clin. Pharmacol. Ther. 110, 542–545 (2021). W. McLaren, L. Gil, S. E. Hunt, H. S. Riat, G. R. S. Ritchie, A. Thormann, P. Flicek, F. Cunningham, The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016). S. Chen, L. C. Francioli, J. K. Goodrich, R. L. Collins, M. Kanai, Q. Wang, J. Alf\" oldi, N. A. Watts, C. Vittal, L. D. Gauthier, T. Poterba, M. W. Wilson, Y. Tarasova, W. Phu, R. Grant, M. T. Yohannes, Z. Koenig, Y. Farjoun, E. Banks, S. Donnelly, S. Gabriel, N. Gupta, S. Ferriera, C. Tolonen, S. Novod, L. Bergelson, D. Roazen, V. Ruano-Rubio, M. Covarrubias, C. Llanwarne, N. Petrillo, G. Wade, T. Jeandet, R. Munshi, K. Tibbetts, A. O’Donnell-Luria, M. Solomonson, C. Seed, A. R. Martin, M. E. Talkowski, H. L. Rehm, M. J. Daly, G. Tiao, B. M. Neale, D. G. MacArthur, K. J. Karczewski, A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024). J. T. Robinson, H. Thorvaldsd\’ ottir, W. Winckler, M. Guttman, E. S. Lander, G. Getz, J. P. Mesirov, Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011). C. F. Thorn, E. Aklillu, T. E. Klein, R. B. Altman, PharmGKB summary: very important pharmacogene information for CYP1A2. Pharmacogenet. Genomics 22, 73–77 (2012). V. Siokas, D. S. Mouliou, I. Liampas, A.-M. Aloizou, V. Folia, E. Zoupa, A. Papadimitriou, E. Lavdas, D. P. Bogdanos, E. Dardiotis, Analysis of ADORA2A rs5760423 and CYP1A2 rs762551 Genetic Variants in Patients with Alzheimer’s Disease. Int. J. Mol. Sci. 23, 14400 (2022). H. Wang, Z. Zhang, S. Han, Y. Lu, F. Feng, J. Yuan, CYP1A2 rs762551 polymorphism contributes to cancer susceptibility: a meta-analysis from 19 case-control studies. BMC Cancer 12, 528 (2012). K.-M. Lin, H.-H. Tsou, I.-J. Tsai, M.-C. Hsiao, C.-F. Hsiao, C.-Y. Liu, W. W. Shen, H.-S. Tang, C.-K. Fang, C.-S. Wu, S.-C. Lu, H.-W. Kuo, S. C. Liu, H.-W. Chan, Y.-T. Hsu, J.-N. Tian, Y.-L. Liu, CYP1A2 genetic polymorphisms are associated with treatment response to the antidepressant paroxetine. Pharmacogenomics 11, 1535–1543 (2010). M. Viikki, O. Kampman, N. Sepp\" al\" a, N. Mononen, T. Lehtim\" aki, E. Leinonen, CYP1A2polymorphism \textminus1545C \textgreater T (rs2470890) is associated with increased side effects to clozapine. BMC Psychiatry 14, 50 (2014). M. Pitarque, O. von Richter, B. Oke, H. Berkkan, M. Oscarson, M. Ingelman-Sundberg, Identification of a single nucleotide polymorphism in the TATA box of the CYP2A6 gene: impairment of its promoter activity. Biochem. Biophys. Res. Commun. 284, 455–460 (2001). E. M. McDonagh, C. Wassenaar, S. P. David, R. F. Tyndale, R. B. Altman, M. Whirl-Carrillo, T. E. Klein, PharmGKB Summary - Very Important Pharmacogene Information for Cytochrome P-450, Family 2, Subfamily A, polypeptide 6 (CYP2A6). Pharmacogenet. Genomics 22, 695–708 (2012). A. W. R. Langlois, M. J. Chenoweth, D. Twesigomwe, G. Scantamburlo, M. Whirl-Carrillo, K. Sangkuhl, T. E. Klein, C. Nofziger, R. F. Tyndale, A. Gaedigk, PharmVar GeneFocus: CYP2A6. Clin. Pharmacol. Ther. 116, 948–962 (2024). K. Mang\’ o, \’ Ad\’ am Ferenc Kiss, F. Fekete, R. eka Erdős, K. Monostory, CYP2B6 allelic variants and non-genetic factors influence CYP2B6 enzyme function. Sci. Rep. 12, 2984 (2022). C. L. Aquilante, M. Niemi, L. Gong, R. B. Altman, T. E. Klein, PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 8. Pharmacogenet. Genomics 23, 721–728 (2013). M. D. Camara, Y. Zhou, T. N. De Sousa, J. P. Gil, A. A. Djimde, V. M. Lauschke, Meta-analysis of the global distribution of clinically relevant CYP2C8 alleles and their inferred functional consequences. Hum. Genomics 18, 40 (2024). S. Haeggström, M. Ingelman-Sundberg, S. Pääbo, H. Zeberg, The clinically relevant CYP2C8*3 and CYP2C9*2 haplotype is inherited from Neandertals. Pharmacogenomics J. 22, 247–249 (2022). V. M. Pratt, L. H. Cavallari, A. L. Del Tredici, H. Hachad, Y. Ji, A. M. Moyer, S. A. Scott, M. Whirl-Carrillo, K. E. Weck, Recommendations for Clinical CYP2C9 Genotyping Allele Selection. J. Mol. Diagn. JMD 21, 746–755 (2019). D. Sibbing, D. Gebhard, W. Koch, S. Braun, J. Stegherr, T. Morath, N. Von Beckerath, J. Mehilli, A. Schömig, T. Schuster, A. Kastrati, Isolated and interactive impact of common CYP2C19 genetic variants on the antiplatelet effect of chronic clopidogrel therapy. J. Thromb. Haemost. JTH 8, 1685–1693 (2010). L. M. de Jong, S. Boussallami, E. Sánchez-López, M. Giera, M. E. Tushuizen, M. Hoekstra, L. J. A. C. Hawinkels, R. Rissmann, J. J. Swen, M. L. Manson, The impact of CYP2C19 genotype on phenoconversion by concomitant medication. Front. Pharmacol. 14, 1201906 (2023). A. Krishna, M. Biryukov, C. Trefois, P. M. A. Antony, R. Hussong, J. Lin, M. Heinäniemi, G. Glusman, S. Köglsberger, O. Boyd, B. H. J. van den Berg, D. Linke, D. Huang, K. Wang, L. Hood, A. Tholey, R. Schneider, D. J. Galas, R. Balling, P. May, Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson’s disease. BMC Genomics 15, 1154 (2014). C. Nofziger, A. J. Turner, K. Sangkuhl, M. Whirl-Carrillo, J. A. G. Agúndez, J. L. Black, H. M. Dunnenberger, G. Ruano, M. A. Kennedy, M. S. Phillips, H. Hachad, T. E. Klein, A. Gaedigk, PharmVar GeneReview: CYP2D6. Clin. Pharmacol. Ther. 107, 154–170 (2020). L. Zhu, Y. He, F. Niu, M. Yan, J. Li, D. Yuan, T. Jin, Polymorphisms of drug-metabolizing enzyme CYP2E1 in Chinese Uygur population. Medicine (Baltimore) 97, e9970 (2018). M. Kumondai, E. M. G. Rico, E. Hishinuma, A. Ueda, S. Saito, D. Saigusa, S. Tadaka, K. Kinoshita, T. Nakayoshi, A. Oda, A. Abe, M. Maekawa, N. Mano, N. Hirasawa, M. Hiratsuka, Functional Characterization of 40 CYP3A4 Variants by Assessing Midazolam 1′-Hydroxylation and Testosterone 6β-Hydroxylation. Drug Metab. Dispos. 49, 212–220 (2021). K. S. Praveenkumar, V. Yogalakshmi, G. Nandita, C. George Priya Doss, G. Sangeetha, E. Sudha, C. D. Mohana Priya, Impact of CYP3A5 rs776746 and CYP3A7 rs2257401 polymorphism on steroid metabolism and dose optimization in pediatric nephrotic syndrome. Med. Drug Discov. 27, 100216 (2025). K. A. Birdwell, B. Decker, J. M. Barbarino, J. F. Peterson, C. M. Stein, W. Sadee, D. Wang, A. A. Vinks, Y. He, J. J. Swen, J. S. Leeder, R. van Schaik, K. E. Thummel, T. E. Klein, K. E. Caudle, I. a. M. MacPhee, Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing. Clin. Pharmacol. Ther. 98, 19–24 (2015). K. Klaus, M. Vaht, K. Pennington, J. Harro, Interactive effects of DRD2 rs6277 polymorphism, environment and sex on impulsivity in a population-representative study. Behav. Brain Res. 403, 113131 (2021). J. Choi, K. G. Tantisira, Q. L. Duan, Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes. Pharmacogenomics J. 19, 127–135 (2019). J. Chen, B. K. Lipska, N. Halim, Q. D. Ma, M. Matsumoto, S. Melhem, B. S. Kolachana, T. M. Hyde, M. M. Herman, J. Apud, M. F. Egan, J. E. Kleinman, D. R. Weinberger, Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am. J. Hum. Genet. 75, 807–821 (2004). T. Ishii, R. Wakabayashi, H. Kurosaki, A. Gemma, K. Kida, Association of serotonin transporter gene variation with smoking, chronic obstructive pulmonary disease, and its depressive symptoms. J. Hum. Genet. 56, 41–46 (2011). J. Yuan, C. Kang, M. Wang, Q. Wang, P. Li, H. Liu, Y. Hou, P. Su, F. Yang, Y. Wei, J. Yang, Association Study of Serotonin Transporter SLC6A4 Gene with Chinese Han Irritable Bowel Syndrome. PLoS ONE 9, e84414 (2014). N. N. Vijayan, Y. Iwayama, L. V. Koshy, C. Natarajan, C. Nair, P. M. Allencherry, T. Yoshikawa, M. Banerjee, Evidence of association of serotonin transporter gene polymorphisms with schizophrenia in a South Indian population. J. Hum. Genet. 54, 538–542 (2009). M.-A. Enoch, C. A. Hodgkinson, E. Gorodetsky, D. Goldman, A. Roy, Independent effects of 5 and 3 functional variants in the serotonin transporter gene on suicidal behavior in the context of childhood trauma. J. Psychiatr. Res. 47, 900–907 (2013). V. Yuferov, E. R. Butelman, M. Randesi, W. Van Den Brink, P. Blanken, J. M. Van Ree, M. J. Kreek, Association of Serotonin Transporter (SERT) Polymorphisms with Opioid Dependence and Dimensional Aspects of Cocaine Use in a Caucasian Cohort of Opioid Users. Neuropsychiatr. Dis. Treat. Volume 17, 659–670 (2021). E. Szostak, F. atima Gebauer, Translational control by 3-UTR-binding proteins. Brief. Funct. Genomics 12, 58–65 (2013). K. Leppek, R. Das, M. Barna, Functional 5 UTR mRNA structures in eukaryotic translation regulation and how to find them. Nat. Rev. Mol. Cell Biol. 19, 158–174 (2018). R. M. Smith, A. C. Papp, A. Webb, C. L. Ruble, L. M. Munsie, L. K. Nisenbaum, J. E. Kleinman, B. K. Lipska, W. Sadee, Multiple regulatory variants modulate expression of 5-hydroxytryptamine 2A receptors in human cortex. Biol. Psychiatry 73, 546–554 (2013). Y. Sun, S. Tao, S. Tian, J. Shao, Z. Mo, X. Wang, H. Wang, P. Zhao, Z. Chen, Z. Yao, Q. Lu, Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response. J. Affect. Disord. 283, 130–138 (2021). C.-F. Kao, P.-H. Kuo, Y. W.-Y. Yu, A. C. Yang, E. Lin, Y.-L. Liu, S.-J. Tsai, Gene-Based Association Analysis Suggests Association of HTR2A With Antidepressant Treatment Response in Depressed Patients. Front. Pharmacol. 11 (2020). J. Sachau, H. Bruckmueller, J. Gierthm\" uhlen, W. Magerl, D. May, A. Binder, J. Forstenpointner, J. Koetting, C. Maier, T. R. T\" olle, R.-D. Treede, A. Berthele, A. Caliebe, C. Diesch, H. Flor, V. Huge, C. Maih\" ofner, S. Rehm, D. Kersebaum, S.-C. Fabig, J. Vollert, R. Rolke, S. Stemmler, C. Sommer, A. Westermann, I. Cascorbi, R. Baron, The serotonin receptor 2A (HTR2A) rs6313 variant is associated with higher ongoing pain and signs of central sensitization in neuropathic pain patients. Eur. J. Pain Lond. Engl. 25, 595–611 (2021). Y. Wang, X. Tan, Z. Chen, B. Zhang, Y. Gao, Y. Wang, Association between the rs6313 polymorphism in the 5-HTR2A gene and the efficacy of antipsychotic drugs. BMC Psychiatry 23, 682 (2023). P. Gass\’ o, A. Bl\’ azquez, N. Rodr\’ iguez, D. Boloc, T. Torres, S. Mas, A. Lafuente, L. L\’ azaro, Further Support for the Involvement of Genetic Variants Related to the Serotonergic Pathway in the Antidepressant Response in Children and Adolescents After a 12-Month Follow-Up: Impact of the HTR2A rs7997012 Polymorphism. J. Child Adolesc. Psychopharmacol. 28, 711–718 (2018). T. B. Gonz\’ alez-Castro, Y. in Hernandez-Diaz, I. E. Ju\’ arez-Rojop, L. L\’ opez-Narv\’ aez, C. A. Tovilla-Z\’ arate, J. e M. Rodriguez-Perez, J. P. S\’ anchez-de la Cruz, The role of the Cys23Ser (rs6318) polymorphism of the HTR2C gene in suicidal behavior: systematic review and meta-analysis. Psychiatr. Genet. 27, 199–209 (2017). H. Wang, G. Jin, H. Wang, G. Liu, J. Qian, L. Jin, Q. Wei, H. Shen, W. Huang, D. Lu, Genetic susceptibility of lung cancer associated with common variants in the 3 untranslated regions of the adenosine triphosphate-binding cassette B1 (ABCB1) and ABCC1 candidate transporter genes for carcinogen export. Cancer 115, 595–607 (2009). D. Campa, J. Sainz, B. Pardini, L. Vodickova, A. Naccarati, A. Rudolph, J. Novotny, A. F\" orsti, S. Buch, W. Sch\" onfels, C. Schafmayer, H. V\" olzke, M. Hoffmeister, B. Frank, R. Barale, K. Hemminki, J. Hampe, J. Chang-Claude, H. Brenner, P. Vodicka, F. Canzian, A Comprehensive Investigation on Common Polymorphisms in the MDR1/ABCB1 Transporter Gene and Susceptibility to Colorectal Cancer. PLOS ONE 7, e32784 (2012). G. eraldine Dessilly, N. Panin, L. Elens, V. Haufroid, J.-B. Demoulin, Impact of ABCB1 1236C \textgreater T-2677G \textgreater T-3435C \textgreater T polymorphisms on the anti-proliferative activity of imatinib, nilotinib, dasatinib and ponatinib. Sci. Rep. 6, 29559 (2016). L. Magarbeh, C. Hassel, M. Choi, F. Islam, V. S. Marshe, C. C. Zai, R. Zuberi, R. S. Gammal, X. Men, M. Scherf-Clavel, D. Enko, B. N. Frey, R. Milev, C. N. Soares, S. V. Parikh, F. Placenza, S. C. Strother, S. Hassel, V. H. Taylor, F. Leri, P. Blier, F. Farzan, R. W. Lam, G. Turecki, J. A. Foster, S. Rotzinger, S. Kloiber, J. L. Kennedy, S. H. Kennedy, C. A. Bousman, D. J. M\" uller, ABCB1 Gene Variants and Antidepressant Treatment Outcomes: A Systematic Review and Meta-Analysis Including Results from the CAN-BIND-1 Study. Clin. Pharmacol. Ther. 114, 88–117 (2023). S. Wolking, E. Schaeffeler, H. Lerche, M. Schwab, A. T. Nies, Impact of Genetic Polymorphisms of ABCB1 (MDR1, P-Glycoprotein) on Drug Disposition and Potential Clinical Implications: Update of the Literature. Clin. Pharmacokinet. 54, 709–735 (2015). Y. Zhang, Z. Wang, Y. Wang, W. Jin, Z. Zhang, L. Jin, J. Qian, L. Zheng, CYP3A4 and CYP3A5: the crucial roles in clinical drug metabolism and the significant implications of genetic polymorphisms. PeerJ 12, e18636 (2024). M. Ingelman-Sundberg, Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J. 5, 6–13 (2005). C. Nofziger, A. J. Turner, K. Sangkuhl, M. Whirl-Carrillo, J. e A. G. Ag\’ undez, J. L. Black, H. M. Dunnenberger, G. Ruano, M. A. Kennedy, M. S. Phillips, H. Hachad, T. E. Klein, A. Gaedigk, PharmVar GeneReview: CYP2D6. Clin. Pharmacol. Ther. 107, 154–170 (2020). Y. Yang, M. R. Botton, E. R. Scott, S. A. Scott, Sequencing the CYP2D6 gene: from variant allele discovery to clinical pharmacogenetic testing. Pharmacogenomics 18, 673–685 (2017). W. Kuban, W. A. Daniel, Cytochrome P450 expression and regulation in the brain. Drug Metab. Rev. 53, 1–29 (2021). D.-D. Tian, S. Natesan, J. R. White, M. F. Paine, Effects of Common CYP1A2 Genotypes and Other Key Factors on Intraindividual Variation in the Caffeine Metabolic Ratio: An Exploratory Analysis. Clin. Transl. Sci. 12, 39–46 (2019). C. E. Staatz, L. K. Goodman, S. E. Tett, Effect of CYP3A and ABCB1 single nucleotide polymorphisms on the pharmacokinetics and pharmacodynamics of calcineurin inhibitors: Part I. Clin. Pharmacokinet. 49, 141–175 (2010). M. van der Lee, W. J. Rowell, R. Menafra, H.-J. Guchelaar, J. J. Swen, S. Y. Anvar, Application of long-read sequencing to elucidate complex pharmacogenomic regions: a proof of principle. Pharmacogenomics J. 22, 75–81 (2022). Tables Tables are available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Supplementarymateriallegends.docx FigureS1.pdf Figure S1 FigureS2.tif Figure S2 FigureS3.tif Figure S3 Table1.docx Table 1. Variants within the CYP1A2 locus. Table2.docx Table 2. Genotype-to-phenotype interpretation for major pharmacogenes in SH-SY5Y: star-allele diplotypes and relative functional expectations. Genotype-derived phenotypes were interpreted as relative functional expectations, recognizing that cell-line expression context may differ. Cite Share Download PDF Status: Under Review Version 1 posted Review # 2 received at journal 28 Apr, 2026 Review # 1 received at journal 22 Apr, 2026 Reviewer # 2 agreed at journal 07 Apr, 2026 Reviewer # 1 agreed at journal 07 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 Unknown event 26 Mar, 2026 Editor assigned by journal 25 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9224585","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":618411896,"identity":"d5598c65-45e5-4344-820c-a91c82d043e5","order_by":0,"name":"Wojciech Kuban","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACCQYeIFkhwQ/hHgCJgEABIS1nJCQbULUYENDC2MZAghbJGbkHPxfOs5BgkG5+9oDhzD15yfYeA+YCPFqkJfKSpWduk5BgkDlmbsBwo9hwNs8ZA+YZeLTISeQYSPNuk6izv5FgJsHwIYFxnkTuBmYe/FqMf/POAdoikf4NpMWeoBZpiRwzad4GkJYcoC03EhJnE9Ii2fMuzZrnGMgvZ8okEs4kJM/sOf/hMD6/SBzPPXybp6YOGGLt2yQ+HEuwnXG8LfFxQQVuLUiagTgByj5MjAZYFEIAM3FaRsEoGAWjYIQAAH/eRte3l+M6AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0401-1714","institution":"Maj Institute of Pharmacology, Polish Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Wojciech","middleName":"","lastName":"Kuban","suffix":""},{"id":618411897,"identity":"d7830cfa-ddd7-4b17-801e-334f80486b46","order_by":1,"name":"Paula Konowalska","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Konowalska","suffix":""},{"id":618411898,"identity":"8b6278c3-0ef6-475a-994e-e410c6b934b8","order_by":2,"name":"Malgorzata Borczyk","email":"","orcid":"https://orcid.org/0000-0002-4304-8384","institution":"Laboratory of Pharmacogenomics, Maj Institute of Pharmacology PAS","correspondingAuthor":false,"prefix":"","firstName":"Malgorzata","middleName":"","lastName":"Borczyk","suffix":""},{"id":618411899,"identity":"04802869-15c9-4285-8bbe-7a6d39bdf5e3","order_by":3,"name":"Marcin Piechota","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marcin","middleName":"","lastName":"Piechota","suffix":""},{"id":618411900,"identity":"41679955-8c59-46d7-856d-0e84c724221e","order_by":4,"name":"Michal Korostyński","email":"","orcid":"https://orcid.org/0000-0002-4273-7401","institution":"Maj Institute of Pharmacology PAS","correspondingAuthor":false,"prefix":"","firstName":"Michal","middleName":"","lastName":"Korostyński","suffix":""},{"id":618411901,"identity":"3eb9c260-193f-47f2-b9aa-8bf70d1768db","order_by":5,"name":"Władysława Daniel","email":"","orcid":"","institution":"Polish Academy of Sciences, Krakow, Poland","correspondingAuthor":false,"prefix":"","firstName":"Władysława","middleName":"","lastName":"Daniel","suffix":""}],"badges":[],"createdAt":"2026-03-25 14:48:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9224585/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9224585/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106726702,"identity":"3b90d953-eb4f-44b1-8320-dd2ba613072c","added_by":"auto","created_at":"2026-04-12 18:37:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4675365,"visible":true,"origin":"","legend":"\u003cp\u003eCytogenetic ideograms with determined pharmacogene mapping and genomic context tracks (GRCh38/hg38) in this study. Respective chromosomes are shown as cytoband ideograms derived from UCSC hg38. Giemsa banding patterns are encoded by stain class (gneg, light; gpos, darker intensity with increasing condensation), with centromeres (acen) highlighted and acrocentric stalk/satellite regions (stalk) indicated on chromosomes 13, 14, 15, 21 and 22. Blue triangles mark pharmacogene locations placed using cytogenetic nomenclature; each marker corresponds to the geometric midpoint of the reported cytogenetic band (e.g., 10q23.33), providing a band-level positional proxy rather than an exact transcription start site. Two left-side heatmap tracks summarize genomic context. The outer track shows gene density (refGene models per 1 Mb; UCSC Table Browser), with higher density represented by brighter signal. The inner track shows GC content (percent GC in 200 kb windows; UCSC hg38 GC BigWig), with higher GC represented by brighter signal. All chromosomes are aligned at the centromere (0; dashed line), and the y-axis represents distance from the centromere in base pairs, enabling direct comparison of relative p-arm (above) and q-arm (below) organization across chromosomes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/64822269788cfe1713320e5d.png"},{"id":106619517,"identity":"7f0aade7-d7f6-4710-ac56-791b9628bff6","added_by":"auto","created_at":"2026-04-10 13:51:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2004114,"visible":true,"origin":"","legend":"\u003cp\u003eSaturation of observed variant callouts across CYP2A6. Schematic of the CYP2A6 locus (Chr19) showing the gene model (exons/UTRs) and promoter annotation, with rsID callouts marking variants detected in our call set. The dense distribution of callouts across flanking and intronic intervals, with additional sites proximate to UTR/exonic segments, illustrates high locus-level variant saturation rather than isolated single-site events. Promoter-proximal positions are highlighted to emphasize potential regulatory context.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/e8a1f3798984592e8f47b65f.png"},{"id":106726029,"identity":"b2468d12-d069-4cc7-9c52-4c496717b14f","added_by":"auto","created_at":"2026-04-12 18:35:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5418183,"visible":true,"origin":"","legend":"\u003cp\u003eCYP2C19 locus schematic with dense variant callouts in SH-SY5Y. Gene model for CYP2C19 (GRCh38) showing annotated exons (CDS/UTR) and the upstream promoter/flank region. Vertical tick marks indicate the positions of variants detected in our SH-SY5Y call set, labeled by rsID, illustrating a high density of primarily non-coding polymorphisms distributed across intronic and flanking sequences. The callout pattern includes variants consistent with the CYP2C19*17 haplotype (promoter/regulatory increased-function background), supporting interpretation of SH-SY5Y as a CYP2C19 ultrarapid model in genotype-informed psychopharmacology assays.\u003c/p\u003e","description":"","filename":"Figura3.png","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/dfc84f0fb0af99a85c118b72.png"},{"id":106619519,"identity":"e2bcca24-6969-4c33-891a-5b704b027d3d","added_by":"auto","created_at":"2026-04-10 13:51:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1588182,"visible":true,"origin":"","legend":"\u003cp\u003eCYP3A5 locus schematic with observed variant callouts in SH-SY5Y. Gene model for CYP3A5 (GRCh38) showing annotated exons (CDS/UTR), the transcription start site (TSS), the translation start codon (ATG), and the promoter window (-688.49 bp from TSS). Vertical tick marks denote variant positions observed in our SH-SY5Y call set, labeled by rsID. The key functional site rs776746 (the defining variant for CYP3A5*3, associated with aberrant splicing and loss of CYP3A5 expression) is highlighted among predominantly non-coding calls, consistent with a CYP3A5 *3/*3 background and no additional coding loss-of-function signals within the set shown.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/e1d4889499da0f8b64b66be8.png"},{"id":106959049,"identity":"6fb1345e-b498-4619-888d-dfcafc9340d3","added_by":"auto","created_at":"2026-04-15 08:44:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14250759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/a3e24d60-8a3e-49bc-931c-0401f4e74a4e.pdf"},{"id":106619516,"identity":"47ade8c3-9e3c-4ab2-85f0-3f20119631fa","added_by":"auto","created_at":"2026-04-10 13:51:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementarymateriallegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/50b026e4ac2be07798bd4204.docx"},{"id":106725916,"identity":"ad56cd6f-0be7-422f-987c-3af7d9ad6562","added_by":"auto","created_at":"2026-04-12 18:34:28","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10379,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/33cb30f857f0005911971d01.pdf"},{"id":106619525,"identity":"f8398d78-61bd-4c3b-a6f4-7d7790c04aaf","added_by":"auto","created_at":"2026-04-10 13:51:05","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":369671,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/dc1b885f646fbc00a6544007.tif"},{"id":106619521,"identity":"225502f6-a95c-4970-9027-1dc240bb154f","added_by":"auto","created_at":"2026-04-10 13:51:05","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":466616,"visible":true,"origin":"","legend":"Figure S3","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/01b2adb2e94400fc62547720.tif"},{"id":106619523,"identity":"e0b91130-8c6a-4bff-b6e2-5eb7fc7bc35f","added_by":"auto","created_at":"2026-04-10 13:51:05","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eVariants within the CYP1A2 locus.\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/00d2cd250ac69cc24453c71b.docx"},{"id":106619524,"identity":"75c885f3-a980-4bce-ae1b-a733ac421d25","added_by":"auto","created_at":"2026-04-10 13:51:05","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e2\u003c/strong\u003e. Genotype-to-phenotype interpretation for major pharmacogenes in SH-SY5Y: star-allele diplotypes and relative functional expectations. Genotype-derived phenotypes were interpreted as relative functional expectations, recognizing that cell-line expression context may differ.\u003c/p\u003e","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9224585/v1/ad9f7b7278328534e7a25f19.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"The SH-SY5Y Pharmacogenome Resolved by Long-Read Whole-Genome Sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeuropsychiatric disorders remain difficult to model mechanistically and to treat effectively, in part because of drug response and adverse effects vary widely across individuals. \u003cem\u003eIn vitro\u003c/em\u003e systems, especially human neuronal cell lines, support the bridge from discovery to translational pharmacology. The SH-SY5Y neuroblastoma cell line is widely used due to its human origin, capacity for neuronal differentiation, and ability to reproduce selected aspects of central nervous system pharmacology (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). SH-SY5Y cells (SH-SY5Y) express multiple neurotransmitter receptors and transporters and can be directed toward dopaminergic or cholinergic phenotypes, enabling applications in target validation, drug screening, mechanistic studies, and toxicity testing (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite extensive use, SH-SY5Y has not been systematically profiled for genetic variation in genes with direct pharmacogenomic relevance. This gap limits interpretability and cross-study comparability, particularly when experiments involve drugs whose disposition and pharmacodynamics are strongly genotype-dependent. Cytochrome P450 (CYP) enzymes are central determinants of interindividual variability in psychopharmacological treatment and constitute the largest phase I drug-metabolizing enzyme family (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKey pharmacogenes relevant to psychopharmacology include hepatic and extrahepatic CYPs as well as major neuropsychiatric targets and transport pathways. CYP1A2 contributes substantially to hepatic CYP content and metabolizes caffeine, clozapine, and other psychoactive agents; its activity is modulated by environmental exposures (e.g., smoking) and transcriptional/epigenetic regulation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). CYP2A6 influences nicotine clearance with clinical implications for smoking behavior and cessation outcomes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). CYP2B6 metabolizes efavirenz and methadone and shows marked interindividual variability driven by polymorphism and alternative splicing (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The CYP2C cluster (10q24) includes CYP2C8 and CYP2C9, which contribute to clearance of multiple therapeutic drug classes and are regulated by nuclear receptors such as pregnane X receptor and constitutive androstane receptor (PXR and CAR, respectively) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while CYP2C19 is a clinically prominent pharmacogene affecting clopidogrel, proton pump inhibitors, antidepressants and antipsychotics, with established consequences of loss- and gain-of-function alleles (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). CYP2D6 metabolizes approximately a quarter of prescribed drugs, including many antidepressants and antipsychotics; its complex genetic architecture (including copy-number variation) drives the continuum from poor to ultrarapid metabolizer phenotypes, and brain expression may further contribute to neurochemical effects (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Moreover, CYP2C19 and CYP2D6 take part in the metabolism of endogenous substrates including steroid hormones, monoaminergic neurotransmitters and endocannabinoids, affecting in this way brain development and function (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). CYP2E1 links ethanol/metabolic stress to xenobiotic metabolism and oxidative stress pathways (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), whereas CYP3A4 and CYP3A5 account for a large fraction of overall drug metabolism, with CYP3A5 expression varying substantially across individuals due to splice-site polymorphisms that impact dosing of drugs such as tacrolimus (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond drug metabolism, variation in catecholamine \u003cem\u003eO-\u003c/em\u003emethyltranspherase (COMT) modulates catecholamine neurotransmitter inactivation with established links to prefrontal cortical function and complex genetic associations with schizophrenia (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). \u003cem\u003eDRD2\u003c/em\u003e encodes the dopamine D\u003csub\u003e2\u003c/sub\u003e receptor, a principal mediator of antipsychotic efficacy and a key regulator of striatal signaling and reward-related behaviors (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). \u003cem\u003eSLC6A4\u003c/em\u003e encodes the serotonin transporter (SERT/5-HTT), the primary molecular target of selective serotonin reuptake inhibitors\u0026thinsp;\u0026minus;\u0026thinsp;SSRIs (and an important target for many serotonin noradrenaline reuptake inhibitors\u0026thinsp;\u0026minus;\u0026thinsp;SNRIs).Promoter variation at 5-HTTLPR is associated with altered transcription and transporter expression (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). \u003cem\u003eHTR2A\u003c/em\u003e and \u003cem\u003eHTR2C\u003c/em\u003e encode the 5-HT\u003csub\u003e2A\u003c/sub\u003e and 5-HT\u003csub\u003e2C\u003c/sub\u003e receptors, respectively; these receptors represent major pharmacological nodes in cortical and appetite/energy-balance circuits, with \u003cem\u003eHTR2C\u003c/em\u003e additionally shaped by extensive RNA editing and alternative splicing, and repeatedly evaluated in the context of antipsychotic-induced weight gain (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe abovementioned genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) define a core axis for psychopharmacogenomics, spanning drug metabolism, transport, and central pharmacodynamic targets. Here, we aim to refine the utility of SH-SY5Y as an \u003cem\u003ein vitro\u003c/em\u003e model for psycho- and neuropharmacological research by systematically sequencing and annotating variants in these pharmacogenes. This framework is intended to improve interpretability of SH-SY5Y-based experiments, support more reproducible inference about drug disposition and response, and facilitate downstream precision-oriented neuropsychopharmacological research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell line and genomic DNA isolation\u003c/h2\u003e \u003cp\u003eSH-SY5Y human neuroblastoma cells were obtained from ATCC (CRL-2266; lot: 70047955) and harvested at ~\u0026thinsp;80% confluence. High-molecular-weight (HMW) genomic DNA was isolated from cell pellets using the Monarch\u0026reg; HMW DNA Extraction Kit (New England Biolabs) following the manufacturer\u0026rsquo;s protocol, with gentle handling to minimize DNA shearing. DNA was eluted in the supplied buffer and stored at 4\u0026deg;C until library preparation.\u003c/p\u003e \u003cp\u003eDNA concentration was measured by fluorometry (Qubit dsDNA Assay). Purity was assessed by UV spectrophotometry (A260/A280 and A260/A230). HMW DNA integrity and size distribution were verified by pulsed-field gel electrophoresis (or equivalent capillary electrophoresis). Only preparations showing predominantly HMW material with minimal smearing and no evidence of protein/RNA contamination were advanced to library construction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePacBio WOBI whole-genome sequencing\u003c/h3\u003e\n\u003cp\u003eWhole-genome long-read sequencing was performed using the PacBio Human Whole-Genome Sequencing (WOBI) workflow. SMRTbell\u0026reg; libraries were prepared from HMW DNA according to the manufacturer\u0026rsquo;s recommendations and sequenced on a PacBio long-read platform (Revio system) to a target depth of ~\u0026thinsp;30\u0026times;. Circular consensus sequencing (CCS) processing was used to generate HiFi reads.\u003c/p\u003e \u003cp\u003eSequencing output was subjected to standard quality control (QC). Per-read accuracy, read length, and yield metrics were evaluated using PacBio tools (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Only HiFi reads meeting predefined QC thresholds were used for downstream analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eLong-read mapping and variant calling\u003c/h3\u003e\n\u003cp\u003eLong-read data quality was assessed using FastQC (v0.12.1; configured for very long reads) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The dataset showed a median quality score of 39 and a median read length of ~\u0026thinsp;14,000 bp (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). HiFi reads were aligned to GRCh38 using pbmm2, with default parameters (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). SNVs and small indels were called using GATK HaplotypeCaller (v4.6.0.0) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Structural variants (SVs) were called using PBSV (v2.9.0) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and copy-number variants (CNVs) were inferred using HiFiCNV (v1.0.1) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). CNVs spanning target pharmacogenes (CYP1A2\u0026ndash;CYP3A5, COMT, DRD2, SLC6A4, HTR2A, HTR2C, ABCB1) were evaluated by intersecting HiFiCNV and PBSV outputs with gene-level genomic intervals and retaining concordant or otherwise supported calls.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eVariant annotation\u003c/h3\u003e\n\u003cp\u003ePharmacogenetic star-allele calling was performed using Polygenic (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), starting from VCF files for the target pharmacogenes. Polygenic assigns star alleles per gene and reports the corresponding predicted phenotype based on Pharmacogene Variation Consortium (PharmVar) definitions (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). SNVs and small indels were additionally annotated using the Variant Effect Predictor (VEP v113.0) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). VEP annotation was restricted to genes listed in Supplementary Table\u0026nbsp;1 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) plus\u0026thinsp;\u0026plusmn;\u0026thinsp;5 kbp upstream and downstream of each gene boundary. Population allele-frequency annotations were added from gnomAD (v4.1.0) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Read-level evidence was inspected using the Integrative Genomics Viewer (IGV v2.17.4) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Supplementary Fig.\u0026nbsp;3 (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e) shows the representative IGV view of CYP3A5 rs776746 (CYP3A5*3) in SH-SY5Y HiFi WGS data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCYP1A2\u003c/h2\u003e \u003cp\u003eFour variants were identified within the \u003cem\u003eCYP1A2\u003c/em\u003e locus (15q24.1.) in SH-SY5Y (Table\u0026nbsp;1): two intronic variants (rs762551, rs2472304), one synonymous coding variant (rs2470890), and one 3\u0026prime; UTR variant (rs33923017). The variant rs762551 \u003cem\u003e(CYP1A2*1F)\u003c/em\u003e is a well-characterized allele associated with increased \u003cem\u003eCYP1A2\u003c/em\u003e inducibility in the presence of environmental inducers (e.g., cigarette smoke), with downstream effects on drug metabolism (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In SH-SY5Y, the observed allele fraction for rs762551 (AF\u0026thinsp;=\u0026thinsp;0.63) was consistent with the corresponding gnomAD allele frequency, supporting population-relevant representation of this locus (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCYP2A6\u003c/h3\u003e\n\u003cp\u003eAcross the \u003cem\u003eCYP2A6\u003c/em\u003e locus (19q13.2.), 25 SNPs were detected in SH-SY5Y (predominantly upstream/downstream and intronic/non-coding transcript contexts; one synonymous variant was observed; no protein-altering coding variants were identified) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Star-allele calling resolved \u003cem\u003eCYP2A6\u003c/em\u003e as the *9/*1 diplotype, comprising one reduced-function allele (*9) and one reference allele (*1). The \u003cem\u003eCYP2A6*9\u003c/em\u003e allele is defined by the promoter SNP rs28399433 (\u0026minus;\u0026thinsp;48T\u0026thinsp;\u0026gt;\u0026thinsp;G, TATA box), which has been associated with decreased transcription and reduced enzyme expression (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Consistent with curated genotype-to-phenotype mappings, *9/*1 corresponds to an intermediate (reduced) metabolizer status (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Functionally, reduced CYP2A6 activity is expected to decrease clearance of nicotine, coumarin, tegafur, and tobacco-derived nitrosamines (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCYP2B6\u003c/h3\u003e\n\u003cp\u003eAcross the \u003cem\u003eCYP2B6\u003c/em\u003e locus (19q13.2.), 73 SNPs were detected in SH-SY5Y, with annotations dominated by intronic / upstream\u0026ndash;downstream regulatory categories and several 3\u0026prime; UTR / non-coding transcript features; no protein-altering coding variants were observed in the called set. Star-allele calling resolved SH-SY5Y as \u003cem\u003eCYP2B6\u003c/em\u003e*1/*1, consistent with two reference alleles and a predicted normal (extensive) metabolizer phenotype (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCYP2C8\u003c/h2\u003e \u003cp\u003eAcross the \u003cem\u003eCYP2C8\u003c/em\u003e locus (10q23.33.), 85 SNPs were detected in SH-SY5Y, with annotations dominated by intronic/non-coding transcript (including NMD-transcript) features and upstream/downstream regulatory context, plus a small subset mapping to the 3\u0026prime; UTR (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Star-allele calling resolved CYP2C8 as *1/*1, consistent with normal enzymatic function (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Because CYP2C8 resides within the CYP2C gene cluster, functionally relevant alleles can occur on shared haplotypes with neighboring genes (notably CYP2C9), which is relevant when interpreting multi-gene pharmacogenomic backgrounds (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCYP2C9\u003c/h2\u003e \u003cp\u003eAcross the \u003cem\u003eCYP2C9\u003c/em\u003e locus (10q23.33.), 72 SNPs were detected in SH-SY5Y, with annotations dominated by intronic / non-coding transcript (including NMD-transcript) features and upstream/downstream regulatory context, plus several 3\u0026prime; UTR variants. Star-allele calling resolved CYP2C9 as *1/*1, corresponding to two reference alleles. Consistent with PharmVar/clinical curation, *1/*1 supports a normal metabolizer phenotype. Because CYP2C9 resides within the CYP2C gene cluster, linkage disequilibrium with neighboring loci can be relevant for multi-gene interpretation (for example, CYP2C9*2 may co-occur with CYP2C8*3 in some populations), which is explicitly tracked in CPIC supplemental materials for CYP2C9-guided NSAID use (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCYP2C19\u003c/h2\u003e \u003cp\u003eAcross the \u003cem\u003eCYP2C19\u003c/em\u003e locus (10q24.12.), 84 SNPs were detected in SH-SY5Y (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The call set was dominated by intronic/non-coding transcript annotations with additional upstream/downstream variants; a single synonymous variant (rs17885098) and one missense-annotated site (rs3758581) were present in the VEP output, without altering the star-allele assignment. Star-allele calling resolved \u003cem\u003eCYP2C19\u003c/em\u003e as *17/*17, a gain-of-function genotype. The \u003cem\u003e*17\u003c/em\u003e allele is defined by the promoter variant rs12248560 (\u0026minus;\u0026thinsp;806C\u0026thinsp;\u0026gt;\u0026thinsp;T) and has been shown to increase promoter activity, resulting in higher transcription and enzyme expression (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Consistent with established genotype-to-phenotype translation, the \u003cem\u003eCYP2C19*17/*17\u003c/em\u003e genotype corresponds to an ultrarapid metabolizer phenotype, which is expected to increase clearance (and lower exposure) for CYP2C19 substrates, including proton pump inhibitors, diazepam, and several antidepressants (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCYP2D6\u003c/h2\u003e \u003cp\u003eWithin the \u003cem\u003eCYP2D6 locus\u003c/em\u003e (22q13.2.), no SNPs/short variants passing QC were detected in the SH-SY5Y call set. High-coverage sequencing confirmed the CYP2D6 *1/*1 diplotype, consistent with prior copy-number characterization of SH-SY5Y (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This diplotype is consistently translated as a normal metabolizer phenotype and is expected to encode a fully functional enzyme (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Consequently, SH-SY5Y provides a genetically baseline CYP2D6 background, without decreased activity due to common no-/reduced-function alleles or increased activity driven by gene duplications or multiplications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCYP2E1\u003c/h2\u003e \u003cp\u003eAcross the \u003cem\u003eCYP2E1\u003c/em\u003e locus (10q26.3.), 63 variants were detected in SH-SY5Y. The call set was dominated by intronic variants and additional upstream/downstream sites; multiple entries were annotated as non-coding transcript\u0026ndash;related features. A single 5\u0026prime; UTR indel was observed (rs11445593), and two 3\u0026prime; UTR variants were detected (rs2480256, rs2480257). No coding loss-of-function variants were identified, and no missense changes were detected among the curated variants listed here. The CYP2E1 profile in SH-SY5Y cells is most consistent with intact catalytic capacity for canonical substrates (ethanol, acetone, benzene, nitrosamines), while non-coding variation provides plausible routes for more subtle differences in basal expression and inducibility, which should be considered when CYP2E1-dependent endpoints are interpreted (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCYP3A4\u003c/h2\u003e \u003cp\u003eAcross the \u003cem\u003eCYP3A4\u003c/em\u003e locus (7q22.1.), 21 variants were detected in SH-SY5Y. These were predominantly intronic and upstream/downstream variants, with one 5\u0026prime; UTR variant (rs1477357584) and no coding loss-of-function changes in the sites listed here. Star-allele calling resolved \u003cem\u003eCYP3A4\u003c/em\u003e as *1/*1, consistent with two reference alleles and a normal metabolizer background (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCYP3A5\u003c/h2\u003e \u003cp\u003eAcross the \u003cem\u003eCYP3A5\u003c/em\u003e locus (7q22.1.), 5 variants were detected in SH-SY5Y (predominantly non-coding context, including one 5\u0026prime; UTR variant [rs28371764], two 3\u0026prime; UTR variants [rs4646450, rs80148964], and regulatory/intronic annotations). Star-allele calling resolved \u003cem\u003eCYP3A5\u003c/em\u003e as *3/*3, i.e., two no-function alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The defining *3 variant rs776746 (6986A\u0026thinsp;\u0026gt;\u0026thinsp;G) creates an aberrant splice acceptor site that disrupts splicing and effectively abolishes CYP3A5 protein expression, corresponding to a CYP3A5 non-expresser / poor metabolizer phenotype (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Functionally, this genotype predicts minimal to absent CYP3A5 contribution to CYP3A-dependent metabolism in SH-SY5Y, resulting in CYP3A4 predominance within the CYP3A cluster for relevant substrates, such as tacrolimus (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDRD2\u003c/h2\u003e \u003cp\u003eAcross \u003cem\u003eDRD2\u003c/em\u003e (11q23.2.), we observed a dense set of non-coding variants spanning upstream/downstream regions, introns, and both UTRs (83 SNPs). The protein-coding sequence appears intact, as no missense or loss-of-function variants were detected. Two synonymous coding variants were present (rs6277/C957T and rs6275). Although synonymous, both have been repeatedly interrogated as haplotype anchors associated with inter-individual differences in DRD2 expression/availability and neurobehavioral phenotypes, often with context-dependent effects across cohorts and exposures (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). These data support a DRD2 locus in SH-SY5Y in which sequence-disrupting variation is absent, while expression and RNA handling may vary \u003cem\u003evia\u003c/em\u003e UTR and promoter-proximal context, with synonymous anchors (notably rs6277) serving as tractable haplotype markers for downstream interpretation (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCOMT\u003c/h2\u003e \u003cp\u003eVariant calling across the \u003cem\u003eCOMT\u003c/em\u003e locus (22q11.21) revealed a dense, predominantly non-coding variant landscape. Most sites mapped to intronic and flanking upstream/downstream regions, with multiple UTR positions and frequent consequence annotations on alternative \u003cem\u003eCOMT\u003c/em\u003e transcripts (including isoforms predicted to undergo NMD). Coding variation was limited to two canonical sites: the synonymous rs4633 (C\u0026thinsp;\u0026gt;\u0026thinsp;T) and the missense rs4680 (Val158Met; G\u0026thinsp;\u0026gt;\u0026thinsp;A); no additional missense changes and no predicted coding loss-of-function variants were observed.\u003c/p\u003e \u003cp\u003eThe presence of rs4680 (Val158Met) is consistent with the well-described functional COMT axis, where the Met (A) allele is associated with reduced enzyme thermostability and lower COMT activity (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Functional analyses of COMT genetic variation in human brain tissue support genotype-dependent differences in mRNA, protein abundance, and enzymatic activity, providing biological plausibility for activity shifts in rs4680-defined backgrounds (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). In addition, several 3\u0026prime;-UTR variants were present (including rs165599, rs165728, rs165737, rs165774, rs165895), consistent with a haplotype-rich post-transcriptional regulatory region that can mark expression-modulating LD backgrounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSLC6A4\u003c/h2\u003e \u003cp\u003eVariant calls across \u003cem\u003eSLC6A4\u003c/em\u003e (17q11.2) were dominated by non-coding polymorphisms, with most sites mapping to intronic sequence (e.g., the rs2020936/rs2020937/rs2020938 cluster) and a smaller set of UTR-adjacent variants. Several of these markers have been used as linkage disequilibrium (LD) tags across the locus in diverse cohorts (\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). In our call set, rs2066713 (intronic) and rs8071667 (intronic) represent commonly studied variants that have been examined in relation to substance-use and mood-related phenotypes (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), while a single 3\u0026prime;-UTR variant (chr17:30,197,405 G\u0026thinsp;\u0026gt;\u0026thinsp;T) highlights the potential for post-transcriptional regulation via 3\u0026prime;-UTR\u0026ndash;binding proteins (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). We also observed one 5\u0026prime;-UTR/promoter-proximal SNP (rs6354) (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Multiple entries were annotated to non-coding/NMD-associated transcript models (e.g., rs4636964, rs8071583, rs55817931), consistent with the complex isoform architecture reported for this locus and the long-standing evidence that regulatory variation can influence SLC6A4 expression and treatment-related outcomes. No protein-coding missense or predicted loss-of-function variants were detected, suggesting an intact transporter coding sequence in this dataset. Accordingly, any functional impact if present in this cell-line context would be expected to arise primarily from regulatory variants (5\u0026prime;/3\u0026prime; UTR, promoter-proximal) and/or intronic haplotype structure, rather than altered protein sequence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eHTR2A\u003c/h2\u003e \u003cp\u003eVariant calls across \u003cem\u003eHTR2A\u003c/em\u003e (13q14.2) showed a dense predominance of non-coding polymorphisms, with most sites annotated as intronic or flanking (upstream/downstream) variants. Several variants mapped to the 3\u0026prime;-UTR (rs7324017, rs3803189, rs3125) (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), and we also observed promoter/5\u0026prime;-UTR\u0026ndash;proximal variants (rs6310, rs6312), consistent with prior evidence that regulatory variation at this locus can modulate cortical HTR2A expression and has been repeatedly examined in treatment-response contexts (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). The only coding-region change in this call set was rs6313 (T102C), a synonymous variant that has been evaluated across multiple neuropsychiatric and treatment-related phenotypes (e.g., pain traits, autism-related measures, and antipsychotic response), with results that are often cohort- and ancestry-dependent (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). In addition, rs7997012, a commonly studied intronic pharmacogenetic marker in antidepressant response was present (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). No missense or predicted loss-of-function variants were detected, consistent with an intact HTR2A coding sequence; any functional signal is therefore most likely regulatory and haplotype-driven rather than protein-altering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eHTR2C\u003c/h2\u003e \u003cp\u003eWhole-genome variant calls across \u003cem\u003eHTR2C\u003c/em\u003e (Xq23) reveal an extremely dense landscape of non-coding variation, dominated by intronic and upstream/downstream annotations. Among transcript-proximal sites, we observed a single 5\u0026prime;-UTR/promoter-proximal variant (rs518147; \u0026minus;697G/C). Within the protein-coding sequence, only one amino-acid\u0026ndash;altering variant was detected: rs6318 (Cys23Ser), a commonly studied missense polymorphism in HTR2C (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). No additional missense or predicted loss-of-function variants were observed, consistent with preserved coding integrity in the SNV/short-indel call set; any functional effect is therefore more likely regulatory/haplotype- and transcript-context driven than due to coding disruption.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eABCB1\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eABCB1\u003c/em\u003e callset is consistent with a locus (7q21.12.) where observable WGS SNV diversity is largely regulatory/haplotype-tagging, rather than protein-disrupting. Alongside extensive intronic/flanking variation, we observe two 3\u0026prime;-UTR variants (rs3842, rs17064) (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) and one 5\u0026prime;-UTR variant (rs2214102) (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e), any of which could plausibly influence expression through post-transcriptional or promoter-proximal mechanisms, depending on haplotype background and tissue context.\u003c/p\u003e \u003cp\u003eIn coding regions, we identify the classic pharmacogenetic markers rs1128503 (1236C\u0026thinsp;\u0026gt;\u0026thinsp;T) and rs1045642 (3435C\u0026thinsp;\u0026gt;\u0026thinsp;T) (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e) plus the triallelic missense rs2032582 (2677G\u0026thinsp;\u0026gt;\u0026thinsp;T/A) (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). The presence of this trio supports haplotype-based analyses (rather than single-SNP inference), because reported effects on P-gp expression/transport are often modest, substrate-dependent, and tissue-specific, and are frequently interpreted through the linked 1236\u0026ndash;2677\u0026ndash;3435 haplotype structure (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). In aggregate, the coding sequence appears structurally preserved (no LoF calls), and any functional signal in this dataset would most plausibly arise from haplotype context and regulatory variation rather than coding disruption (\u003cem\u003e72\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe generated a high-coverage, long-read genome profile for SH-SY5Y that resolves key pharmacogene haplotypes. Because SH-SY5Y is widely used to model neuronal drug response and toxicity, yet its pharmacogenomic background is often incompletely specified (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), these data provide a baseline for interpreting metabolism-, receptor- and transporter-dependent readouts. Using PacBio HiFi with pharmacogene interpretation (e.g., PharmVar/PharmGKB resources) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), we define star-allele diplotypes across major CYP families, summarize receptor and transporter variations, and highlight loci where non-coding diversity may affect gene expression without implying coding disruption.\u003c/p\u003e \u003cp\u003eCYP3A4 and CYP3A5 contribute to the metabolism of many clinically used drugs including psychotropics, and endogenous steroids (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). SH-SY5Y carries \u003cem\u003eCYP3A4*1/*1\u003c/em\u003e (consistent with a \u0026ldquo;normal\u0026rdquo; \u003cem\u003eCYP3A4\u003c/em\u003e genotype) and \u003cem\u003eCYP3A5*3/*3\u003c/em\u003e, a non-expresser configuration that minimizes CYP3A5 contribution (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). This predicts CYP3A4-dominant CYP3A metabolism in SH-SY5Y and simplifies CYP3A4-focused functional tests (e.g., inhibitor/inducer studies)(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCYP2C19*17/*17\u003c/em\u003e predicts an ultrarapid metabolism background for CYP2C19 substrates including psychotropics and estrogen steroids (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and is clinically actionable in CPIC contexts (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In cell-line pharmacology, a practical consequence is that CYP2C19-cleared compounds may show faster turnover than a \u0026ldquo;normal\u0026rdquo; CYP2C19 background and lower parent drug exposure (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, \u003cem\u003eCYP2D6*1/*1\u003c/em\u003e (single copy) supports a baseline \u0026ldquo;normal\u0026rdquo; \u003cem\u003eCYP2D6\u003c/em\u003e genotype without duplication-driven ultrarapid metabolism (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). This is particularly relevant because CYP2D6 contributes to the metabolism of many psychotropic drugs and endogenous neuroactive substrates (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e) and supports cleaner probe-substrate and inhibitor study designs (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt \u003cem\u003eCYP2E1\u003c/em\u003e, we observe extensive intronic/flanking and other non-coding annotations, but no additional missense or predicted loss-of-function variants in the provided call list, consistent with preservation of the protein-coding sequence captured by SNV/short-indel calls. This pattern supports a model where expression/inducibility (rather than catalytic sequence disruption) is the more plausible axis of variability relevant to experiments coupling xenobiotic metabolism with oxidative stress phenotypes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the variant rs762551 \u003cem\u003e(CYP1A2*1F)\u003c/em\u003e found in SH-SY5Y is a well-characterized allele associated with increased \u003cem\u003eCYP1A2\u003c/em\u003e inducibility in the presence of environmental inducers (e.g., cigarette smoke), with downstream effects on drug biotransformation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), endogenous metabolism of sex steroids and melatonin (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe configuration of \u003cem\u003eABCB1\u003c/em\u003e in SH-SY5Y does not imply transporter loss of function, but it supports haplotype-aware interpretation in assays where intracellular exposure affects readouts (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Across \u003cem\u003eDRD2, HTR2A, HTR2C, COMT\u003c/em\u003e, and \u003cem\u003eSLC6A4\u003c/em\u003e, no coding loss-of-function alleles were detected; any effects in this model are therefore more likely regulatory and will require functional follow-up.\u003c/p\u003e \u003cp\u003eConsidering the implications for using the obtained SH-SY5Y pharmacogenomic data in \u0026ldquo;in vitro\u0026rdquo; experiments, it should be emphasized that the present work provides a genotype-defined drug disposition context (\u003cem\u003eCYP2C19*17/*17; CYP3A4\u003c/em\u003e-dominant CYP3A due to \u003cem\u003eCYP3A5*3/*3; CYP2D6\u003c/em\u003e*1/*1, single copy). Thus, CYP2C19-cleared probes may show reduced parent drug exposure and CYP3A studies should be planned around CYP3A4. Because \u003cem\u003eABCB1\u003c/em\u003e carries a common 1236\u0026ndash;2677\u0026ndash;3435 haplotype block rather than loss-of-function, and because CNV/SV can bias apparent zygosity in cell lines, intracellular exposure and genotype calls should be interpreted haplotype- and CNV/SV-aware.\u003c/p\u003e \u003cp\u003eIn conclusion, SH-SY5Y provides a comparatively \u0026ldquo;interpretable\u0026rdquo; pharmacogenomic baseline CYP3A4-dominant CYP3A, normal CYP2D6, and ultrarapid CYP2C19 with transporter and regulatory context (not coding LoF) as the main expected modifiers of intracellular drug exposure and response. Long-read sequencing reduces ambiguity in pharmacogenes with paralogy/repeats and improves star-allele and phase resolution, making the resulting SH-SY5Y genotype directly usable for probe selection and drug exposure interpretation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). However, functional confirmation (CYP isoform-resolved RNA, protein abundance, and probe phenotyping) is required to translate diplotypes and non-coding variation into calibrated activity priors, particularly for loci where regulation/induction may dominate over coding changes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Grant OPUS 23 no 2022/45/B/NZ7/02419 from the National\u003c/p\u003e\n\u003cp\u003eScience Centre, Krak\u0026oacute;w, Poland\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW. Kuban designed the study, acquired data, prepared DNA samples, interpreted the results, and drafted the manuscript; P. Konowalska acquired data and revised the manuscript; M. Borczyk acquired data and revised the manuscript; M. Piechota acquired data and revised the manuscript; M. Korostyński interpreted the results and revised the manuscript; W. A. Daniel designed the study, interpreted the results, and revised the manuscript. All authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePacBio HiFi whole-genome sequencing data generated for SH-SY5Y in this study have been submitted to the NCBI Sequence Read Archive (SRA).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eH. Xicoy, B. Wieringa, G. J. M. Martens, The SH-SY5Y cell line in Parkinson\u0026rsquo;s disease research: a systematic review. \u003cem\u003eMol. Neurodegener.\u003c/em\u003e 12, 10 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Kovalevich, D. Langford, Considerations for the Use of SH-SY5Y Neuroblastoma Cells in Neurobiology. \u003cem\u003eMethods Mol. Biol. Clifton NJ\u003c/em\u003e 1078, 9\u0026ndash;21 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU. M. Zanger, M. Schwab, Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. \u003cem\u003ePharmacol. Ther.\u003c/em\u003e 138, 103\u0026ndash;141 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.-F. Zhou, B. Wang, L.-P. Yang, J.-P. Liu, Structure, function, regulation and polymorphism and the clinical significance of human cytochrome P450 1A2. \u003cem\u003eDrug Metab. Rev.\u003c/em\u003e 42, 268\u0026ndash;354 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ.-A. Tanner, B. Prasad, K. G. Claw, P. Stapleton, A. Chaudhry, E. G. Schuetz, K. E. Thummel, R. F. Tyndale, Predictors of Variation in CYP2A6 mRNA, Protein, and Enzyme Activity in a Human Liver Bank: Influence of Genetic and Nongenetic Factors. \u003cem\u003eJ. Pharmacol. Exp. Ther.\u003c/em\u003e 360, 129\u0026ndash;139 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU. M. Zanger, K. Klein, Pharmacogenetics of cytochrome P450 2B6 (CYP2B6): advances on polymorphisms, mechanisms, and clinical relevance. \u003cem\u003eFront. Genet.\u003c/em\u003e 4 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Kirchheiner, J. Brockm\u0026ouml;ller, Clinical consequences of cytochrome P450 2C9 polymorphisms. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 77, 1\u0026ndash;16 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. L. Mega, S. L. Close, S. D. Wiviott, L. Shen, R. D. Hockett, J. T. Brandt, J. R. Walker, E. M. Antman, W. Macias, E. Braunwald, M. S. Sabatine, Cytochrome p-450 polymorphisms and response to clopidogrel. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e 360, 354\u0026ndash;362 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. R. Lee, J. A. Luzum, K. Sangkuhl, R. S. Gammal, M. S. Sabatine, C. M. Stein, D. F. Kisor, N. A. Limdi, Y. M. Lee, S. A. Scott, J.-S. Hulot, D. M. Roden, A. Gaedigk, K. E. Caudle, T. E. Klein, J. A. Johnson, A. R. Shuldiner, Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 112, 959\u0026ndash;967 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. C. Sim, C. Risinger, M.-L. Dahl, E. Aklillu, M. Christensen, L. Bertilsson, M. Ingelman-Sundberg, A common novel CYP2C19 gene variant causes ultrarapid drug metabolism relevant for the drug response to proton pump inhibitors and antidepressants. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 79, 103\u0026ndash;113 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Miksys, R. F. Tyndale, The Unique Regulation of Brain Cytochrome P450 2 (CYP2) Family Enzymes by Drugs and Genetics. \u003cem\u003eDrug Metab. Rev.\u003c/em\u003e 36, 313\u0026ndash;333 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Haduch, W. A. Daniel, The engagement of brain cytochrome P450 in the metabolism of endogenous neuroactive substrates: a possible role in mental disorders. \u003cem\u003eDrug Metab. Rev.\u003c/em\u003e 50, 415\u0026ndash;429 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. C. Stingl, C. Scholl, J. E. Bosch, R. Viviani, Genetic polymorphism of CYP2C19 and subcortical variability in the human adult brain. \u003cem\u003eTransl. Psychiatry\u003c/em\u003e 11, 467 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. P. Guengerich, Common and Uncommon Cytochrome P450 Reactions Related to Metabolism and Chemical Toxicity. \u003cem\u003eChem. Res. Toxicol.\u003c/em\u003e 14, 611\u0026ndash;650 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Kuehl, J. Zhang, Y. Lin, J. Lamba, M. Assem, J. Schuetz, P. B. Watkins, A. Daly, S. A. Wrighton, S. D. Hall, P. Maurel, M. Relling, C. Brimer, K. Yasuda, R. Venkataramanan, S. Strom, K. Thummel, M. S. Boguski, E. Schuetz, Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. \u003cem\u003eNat. Genet.\u003c/em\u003e 27, 383\u0026ndash;391 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. M. Tunbridge, P. J. Harrison, D. R. Weinberger, Catechol-o-methyltransferase, cognition, and psychosis: Val158Met and beyond. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e 60, 141\u0026ndash;151 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ.-M. Beaulieu, R. R. Gainetdinov, The physiology, signaling, and pharmacology of dopamine receptors. \u003cem\u003ePharmacol. Rev.\u003c/em\u003e 63, 182\u0026ndash;217 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. L. Murphy, P. R. Moya, Human Serotonin Transporter Gene (SLC6A4) Variants: Their Contributions to Understanding Pharmacogenomic and Other Functional G x G and G x E Differences in Health and Disease. \u003cem\u003eCurr. Opin. Pharmacol.\u003c/em\u003e 11, 3\u0026ndash;10 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB. R. Cummins, G. B. Billac, D. E. Nichols, C. D. Nichols, 5-HT2A receptors: Pharmacology and functional selectivity. \u003cem\u003ePharmacol. Rev.\u003c/em\u003e 77, 100059 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacificBiosciences/pbmm2, PacBio (2026); \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PacificBiosciences/pbmm2\u003c/span\u003e\u003cspan address=\"https://github.com/PacificBiosciences/pbmm2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. McKenna, M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis, A. Kernytsky, K. Garimella, D. Altshuler, S. Gabriel, M. Daly, M. A. DePristo, The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. \u003cem\u003eGenome Res.\u003c/em\u003e 20, 1297\u0026ndash;1303 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacificBiosciences/pbsv (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PacificBiosciences/pbsv\u003c/span\u003e\u003cspan address=\"https://github.com/PacificBiosciences/pbsv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacificBiosciences/HiFiCNV (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PacificBiosciences/HiFiCNV\u003c/span\u003e\u003cspan address=\"https://github.com/PacificBiosciences/HiFiCNV\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. Ruzicka, Polygenic SA selection in the UK Biobank. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.6824671\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.6824671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Gaedigk, S. T. Casey, M. Whirl-Carrillo, N. A. Miller, T. E. Klein, Pharmacogene Variation Consortium: A Global Resource and Repository for Pharmacogene Variation. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 110, 542\u0026ndash;545 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW. McLaren, L. Gil, S. E. Hunt, H. S. Riat, G. R. S. Ritchie, A. Thormann, P. Flicek, F. Cunningham, The Ensembl Variant Effect Predictor. \u003cem\u003eGenome Biol.\u003c/em\u003e 17, 122 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Chen, L. C. Francioli, J. K. Goodrich, R. L. Collins, M. Kanai, Q. Wang, J. Alf\\\" oldi, N. A. Watts, C. Vittal, L. D. Gauthier, T. Poterba, M. W. Wilson, Y. Tarasova, W. Phu, R. Grant, M. T. Yohannes, Z. Koenig, Y. Farjoun, E. Banks, S. Donnelly, S. Gabriel, N. Gupta, S. Ferriera, C. Tolonen, S. Novod, L. Bergelson, D. Roazen, V. Ruano-Rubio, M. Covarrubias, C. Llanwarne, N. Petrillo, G. Wade, T. Jeandet, R. Munshi, K. Tibbetts, A. O\u0026rsquo;Donnell-Luria, M. Solomonson, C. Seed, A. R. Martin, M. E. Talkowski, H. L. Rehm, M. J. Daly, G. Tiao, B. M. Neale, D. G. MacArthur, K. J. Karczewski, A genomic mutational constraint map using variation in 76,156 human genomes. \u003cem\u003eNature\u003c/em\u003e 625, 92\u0026ndash;100 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. T. Robinson, H. Thorvaldsd\\\u0026rsquo; ottir, W. Winckler, M. Guttman, E. S. Lander, G. Getz, J. P. Mesirov, Integrative genomics viewer. \u003cem\u003eNat. Biotechnol.\u003c/em\u003e 29, 24\u0026ndash;26 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. F. Thorn, E. Aklillu, T. E. Klein, R. B. Altman, PharmGKB summary: very important pharmacogene information for CYP1A2. \u003cem\u003ePharmacogenet. Genomics\u003c/em\u003e 22, 73\u0026ndash;77 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Siokas, D. S. Mouliou, I. Liampas, A.-M. Aloizou, V. Folia, E. Zoupa, A. Papadimitriou, E. Lavdas, D. P. Bogdanos, E. Dardiotis, Analysis of ADORA2A rs5760423 and CYP1A2 rs762551 Genetic Variants in Patients with Alzheimer\u0026rsquo;s Disease. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e 23, 14400 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Wang, Z. Zhang, S. Han, Y. Lu, F. Feng, J. Yuan, CYP1A2 rs762551 polymorphism contributes to cancer susceptibility: a meta-analysis from 19 case-control studies. \u003cem\u003eBMC Cancer\u003c/em\u003e 12, 528 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK.-M. Lin, H.-H. Tsou, I.-J. Tsai, M.-C. Hsiao, C.-F. Hsiao, C.-Y. Liu, W. W. Shen, H.-S. Tang, C.-K. Fang, C.-S. Wu, S.-C. Lu, H.-W. Kuo, S. C. Liu, H.-W. Chan, Y.-T. Hsu, J.-N. Tian, Y.-L. Liu, CYP1A2 genetic polymorphisms are associated with treatment response to the antidepressant paroxetine. \u003cem\u003ePharmacogenomics\u003c/em\u003e 11, 1535\u0026ndash;1543 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Viikki, O. Kampman, N. Sepp\\\" al\\\" a, N. Mononen, T. Lehtim\\\" aki, E. Leinonen, CYP1A2polymorphism \\textminus1545C \\textgreater T (rs2470890) is associated with increased side effects to clozapine. \u003cem\u003eBMC Psychiatry\u003c/em\u003e 14, 50 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Pitarque, O. von Richter, B. Oke, H. Berkkan, M. Oscarson, M. Ingelman-Sundberg, Identification of a single nucleotide polymorphism in the TATA box of the CYP2A6 gene: impairment of its promoter activity. \u003cem\u003eBiochem. Biophys. Res. Commun.\u003c/em\u003e 284, 455\u0026ndash;460 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. M. McDonagh, C. Wassenaar, S. P. David, R. F. Tyndale, R. B. Altman, M. Whirl-Carrillo, T. E. Klein, PharmGKB Summary - Very Important Pharmacogene Information for Cytochrome P-450, Family 2, Subfamily A, polypeptide 6 (CYP2A6). \u003cem\u003ePharmacogenet. Genomics\u003c/em\u003e 22, 695\u0026ndash;708 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. W. R. Langlois, M. J. Chenoweth, D. Twesigomwe, G. Scantamburlo, M. Whirl-Carrillo, K. Sangkuhl, T. E. Klein, C. Nofziger, R. F. Tyndale, A. Gaedigk, PharmVar GeneFocus: CYP2A6. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 116, 948\u0026ndash;962 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Mang\\\u0026rsquo; o, \\\u0026rsquo; Ad\\\u0026rsquo; am Ferenc Kiss, F. Fekete, R. eka Erdős, K. Monostory, CYP2B6 allelic variants and non-genetic factors influence CYP2B6 enzyme function. \u003cem\u003eSci. Rep.\u003c/em\u003e 12, 2984 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. L. Aquilante, M. Niemi, L. Gong, R. B. Altman, T. E. Klein, PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 8. \u003cem\u003ePharmacogenet. Genomics\u003c/em\u003e 23, 721\u0026ndash;728 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. D. Camara, Y. Zhou, T. N. De Sousa, J. P. Gil, A. A. Djimde, V. M. Lauschke, Meta-analysis of the global distribution of clinically relevant CYP2C8 alleles and their inferred functional consequences. \u003cem\u003eHum. Genomics\u003c/em\u003e 18, 40 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Haeggstr\u0026ouml;m, M. Ingelman-Sundberg, S. P\u0026auml;\u0026auml;bo, H. Zeberg, The clinically relevant CYP2C8*3 and CYP2C9*2 haplotype is inherited from Neandertals. \u003cem\u003ePharmacogenomics J.\u003c/em\u003e 22, 247\u0026ndash;249 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. M. Pratt, L. H. Cavallari, A. L. Del Tredici, H. Hachad, Y. Ji, A. M. Moyer, S. A. Scott, M. Whirl-Carrillo, K. E. Weck, Recommendations for Clinical CYP2C9 Genotyping Allele Selection. \u003cem\u003eJ. Mol. Diagn. JMD\u003c/em\u003e 21, 746\u0026ndash;755 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. Sibbing, D. Gebhard, W. Koch, S. Braun, J. Stegherr, T. Morath, N. Von Beckerath, J. Mehilli, A. Sch\u0026ouml;mig, T. Schuster, A. Kastrati, Isolated and interactive impact of common CYP2C19 genetic variants on the antiplatelet effect of chronic clopidogrel therapy. \u003cem\u003eJ. Thromb. Haemost. JTH\u003c/em\u003e 8, 1685\u0026ndash;1693 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. M. de Jong, S. Boussallami, E. S\u0026aacute;nchez-L\u0026oacute;pez, M. Giera, M. E. Tushuizen, M. Hoekstra, L. J. A. C. Hawinkels, R. Rissmann, J. J. Swen, M. L. Manson, The impact of CYP2C19 genotype on phenoconversion by concomitant medication. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e 14, 1201906 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Krishna, M. Biryukov, C. Trefois, P. M. A. Antony, R. Hussong, J. Lin, M. Hein\u0026auml;niemi, G. Glusman, S. K\u0026ouml;glsberger, O. Boyd, B. H. J. van den Berg, D. Linke, D. Huang, K. Wang, L. Hood, A. Tholey, R. Schneider, D. J. Galas, R. Balling, P. May, Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson\u0026rsquo;s disease. \u003cem\u003eBMC Genomics\u003c/em\u003e 15, 1154 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Nofziger, A. J. Turner, K. Sangkuhl, M. Whirl-Carrillo, J. A. G. Ag\u0026uacute;ndez, J. L. Black, H. M. Dunnenberger, G. Ruano, M. A. Kennedy, M. S. Phillips, H. Hachad, T. E. Klein, A. Gaedigk, PharmVar GeneReview: CYP2D6. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 107, 154\u0026ndash;170 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Zhu, Y. He, F. Niu, M. Yan, J. Li, D. Yuan, T. Jin, Polymorphisms of drug-metabolizing enzyme CYP2E1 in Chinese Uygur population. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e 97, e9970 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Kumondai, E. M. G. Rico, E. Hishinuma, A. Ueda, S. Saito, D. Saigusa, S. Tadaka, K. Kinoshita, T. Nakayoshi, A. Oda, A. Abe, M. Maekawa, N. Mano, N. Hirasawa, M. Hiratsuka, Functional Characterization of 40 CYP3A4 Variants by Assessing Midazolam 1\u0026prime;-Hydroxylation and Testosterone 6β-Hydroxylation. \u003cem\u003eDrug Metab. Dispos.\u003c/em\u003e 49, 212\u0026ndash;220 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. S. Praveenkumar, V. Yogalakshmi, G. Nandita, C. George Priya Doss, G. Sangeetha, E. Sudha, C. D. Mohana Priya, Impact of \u003cem\u003eCYP3A5 rs776746 and CYP3A7\u003c/em\u003e rs2257401 polymorphism on steroid metabolism and dose optimization in pediatric nephrotic syndrome. \u003cem\u003eMed. Drug Discov.\u003c/em\u003e 27, 100216 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. A. Birdwell, B. Decker, J. M. Barbarino, J. F. Peterson, C. M. Stein, W. Sadee, D. Wang, A. A. Vinks, Y. He, J. J. Swen, J. S. Leeder, R. van Schaik, K. E. Thummel, T. E. Klein, K. E. Caudle, I. a. M. MacPhee, Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 98, 19\u0026ndash;24 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Klaus, M. Vaht, K. Pennington, J. Harro, Interactive effects of \u003cem\u003eDRD2\u003c/em\u003e rs6277 polymorphism, environment and sex on impulsivity in a population-representative study. \u003cem\u003eBehav. Brain Res.\u003c/em\u003e 403, 113131 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Choi, K. G. Tantisira, Q. L. Duan, Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes. \u003cem\u003ePharmacogenomics J.\u003c/em\u003e 19, 127\u0026ndash;135 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Chen, B. K. Lipska, N. Halim, Q. D. Ma, M. Matsumoto, S. Melhem, B. S. Kolachana, T. M. Hyde, M. M. Herman, J. Apud, M. F. Egan, J. E. Kleinman, D. R. Weinberger, Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e 75, 807\u0026ndash;821 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Ishii, R. Wakabayashi, H. Kurosaki, A. Gemma, K. Kida, Association of serotonin transporter gene variation with smoking, chronic obstructive pulmonary disease, and its depressive symptoms. \u003cem\u003eJ. Hum. Genet.\u003c/em\u003e 56, 41\u0026ndash;46 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Yuan, C. Kang, M. Wang, Q. Wang, P. Li, H. Liu, Y. Hou, P. Su, F. Yang, Y. Wei, J. Yang, Association Study of Serotonin Transporter SLC6A4 Gene with Chinese Han Irritable Bowel Syndrome. \u003cem\u003ePLoS ONE\u003c/em\u003e 9, e84414 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. N. Vijayan, Y. Iwayama, L. V. Koshy, C. Natarajan, C. Nair, P. M. Allencherry, T. Yoshikawa, M. Banerjee, Evidence of association of serotonin transporter gene polymorphisms with schizophrenia in a South Indian population. \u003cem\u003eJ. Hum. Genet.\u003c/em\u003e 54, 538\u0026ndash;542 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM.-A. Enoch, C. A. Hodgkinson, E. Gorodetsky, D. Goldman, A. Roy, Independent effects of 5 and 3 functional variants in the serotonin transporter gene on suicidal behavior in the context of childhood trauma. \u003cem\u003eJ. Psychiatr. Res.\u003c/em\u003e 47, 900\u0026ndash;907 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Yuferov, E. R. Butelman, M. Randesi, W. Van Den Brink, P. Blanken, J. M. Van Ree, M. J. Kreek, Association of Serotonin Transporter (SERT) Polymorphisms with Opioid Dependence and Dimensional Aspects of Cocaine Use in a Caucasian Cohort of Opioid Users. \u003cem\u003eNeuropsychiatr. Dis. Treat.\u003c/em\u003e Volume 17, 659\u0026ndash;670 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. Szostak, F. atima Gebauer, Translational control by 3-UTR-binding proteins. \u003cem\u003eBrief. Funct. Genomics\u003c/em\u003e 12, 58\u0026ndash;65 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Leppek, R. Das, M. Barna, Functional 5 UTR mRNA structures in eukaryotic translation regulation and how to find them. \u003cem\u003eNat. Rev. Mol. Cell Biol.\u003c/em\u003e 19, 158\u0026ndash;174 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. M. Smith, A. C. Papp, A. Webb, C. L. Ruble, L. M. Munsie, L. K. Nisenbaum, J. E. Kleinman, B. K. Lipska, W. Sadee, Multiple regulatory variants modulate expression of 5-hydroxytryptamine 2A receptors in human cortex. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e 73, 546\u0026ndash;554 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Sun, S. Tao, S. Tian, J. Shao, Z. Mo, X. Wang, H. Wang, P. Zhao, Z. Chen, Z. Yao, Q. Lu, Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response. \u003cem\u003eJ. Affect. Disord.\u003c/em\u003e 283, 130\u0026ndash;138 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC.-F. Kao, P.-H. Kuo, Y. W.-Y. Yu, A. C. Yang, E. Lin, Y.-L. Liu, S.-J. Tsai, Gene-Based Association Analysis Suggests Association of HTR2A With Antidepressant Treatment Response in Depressed Patients. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e 11 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Sachau, H. Bruckmueller, J. Gierthm\\\" uhlen, W. Magerl, D. May, A. Binder, J. Forstenpointner, J. Koetting, C. Maier, T. R. T\\\" olle, R.-D. Treede, A. Berthele, A. Caliebe, C. Diesch, H. Flor, V. Huge, C. Maih\\\" ofner, S. Rehm, D. Kersebaum, S.-C. Fabig, J. Vollert, R. Rolke, S. Stemmler, C. Sommer, A. Westermann, I. Cascorbi, R. Baron, The serotonin receptor 2A (HTR2A) rs6313 variant is associated with higher ongoing pain and signs of central sensitization in neuropathic pain patients. \u003cem\u003eEur. J. Pain Lond. Engl.\u003c/em\u003e 25, 595\u0026ndash;611 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Wang, X. Tan, Z. Chen, B. Zhang, Y. Gao, Y. Wang, Association between the rs6313 polymorphism in the 5-HTR2A gene and the efficacy of antipsychotic drugs. \u003cem\u003eBMC Psychiatry\u003c/em\u003e 23, 682 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Gass\\\u0026rsquo; o, A. Bl\\\u0026rsquo; azquez, N. Rodr\\\u0026rsquo; iguez, D. Boloc, T. Torres, S. Mas, A. Lafuente, L. L\\\u0026rsquo; azaro, Further Support for the Involvement of Genetic Variants Related to the Serotonergic Pathway in the Antidepressant Response in Children and Adolescents After a 12-Month Follow-Up: Impact of the HTR2A rs7997012 Polymorphism. \u003cem\u003eJ. Child Adolesc. Psychopharmacol.\u003c/em\u003e 28, 711\u0026ndash;718 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. B. Gonz\\\u0026rsquo; alez-Castro, Y. in Hernandez-Diaz, I. E. Ju\\\u0026rsquo; arez-Rojop, L. L\\\u0026rsquo; opez-Narv\\\u0026rsquo; aez, C. A. Tovilla-Z\\\u0026rsquo; arate, J. e M. Rodriguez-Perez, J. P. S\\\u0026rsquo; anchez-de la Cruz, The role of the Cys23Ser (rs6318) polymorphism of the HTR2C gene in suicidal behavior: systematic review and meta-analysis. \u003cem\u003ePsychiatr. Genet.\u003c/em\u003e 27, 199\u0026ndash;209 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Wang, G. Jin, H. Wang, G. Liu, J. Qian, L. Jin, Q. Wei, H. Shen, W. Huang, D. Lu, Genetic susceptibility of lung cancer associated with common variants in the 3 untranslated regions of the adenosine triphosphate-binding cassette B1 (ABCB1) and ABCC1 candidate transporter genes for carcinogen export. \u003cem\u003eCancer\u003c/em\u003e 115, 595\u0026ndash;607 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. Campa, J. Sainz, B. Pardini, L. Vodickova, A. Naccarati, A. Rudolph, J. Novotny, A. F\\\" orsti, S. Buch, W. Sch\\\" onfels, C. Schafmayer, H. V\\\" olzke, M. Hoffmeister, B. Frank, R. Barale, K. Hemminki, J. Hampe, J. Chang-Claude, H. Brenner, P. Vodicka, F. Canzian, A Comprehensive Investigation on Common Polymorphisms in the MDR1/ABCB1 Transporter Gene and Susceptibility to Colorectal Cancer. \u003cem\u003ePLOS ONE\u003c/em\u003e 7, e32784 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. eraldine Dessilly, N. Panin, L. Elens, V. Haufroid, J.-B. Demoulin, Impact of ABCB1 1236C \\textgreater T-2677G \\textgreater T-3435C \\textgreater T polymorphisms on the anti-proliferative activity of imatinib, nilotinib, dasatinib and ponatinib. \u003cem\u003eSci. Rep.\u003c/em\u003e 6, 29559 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Magarbeh, C. Hassel, M. Choi, F. Islam, V. S. Marshe, C. C. Zai, R. Zuberi, R. S. Gammal, X. Men, M. Scherf-Clavel, D. Enko, B. N. Frey, R. Milev, C. N. Soares, S. V. Parikh, F. Placenza, S. C. Strother, S. Hassel, V. H. Taylor, F. Leri, P. Blier, F. Farzan, R. W. Lam, G. Turecki, J. A. Foster, S. Rotzinger, S. Kloiber, J. L. Kennedy, S. H. Kennedy, C. A. Bousman, D. J. M\\\" uller, ABCB1 Gene Variants and Antidepressant Treatment Outcomes: A Systematic Review and Meta-Analysis Including Results from the CAN-BIND-1 Study. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 114, 88\u0026ndash;117 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Wolking, E. Schaeffeler, H. Lerche, M. Schwab, A. T. Nies, Impact of Genetic Polymorphisms of ABCB1 (MDR1, P-Glycoprotein) on Drug Disposition and Potential Clinical Implications: Update of the Literature. \u003cem\u003eClin. Pharmacokinet.\u003c/em\u003e 54, 709\u0026ndash;735 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Zhang, Z. Wang, Y. Wang, W. Jin, Z. Zhang, L. Jin, J. Qian, L. Zheng, CYP3A4 and CYP3A5: the crucial roles in clinical drug metabolism and the significant implications of genetic polymorphisms. \u003cem\u003ePeerJ\u003c/em\u003e 12, e18636 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Ingelman-Sundberg, Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. \u003cem\u003ePharmacogenomics J.\u003c/em\u003e 5, 6\u0026ndash;13 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Nofziger, A. J. Turner, K. Sangkuhl, M. Whirl-Carrillo, J. e A. G. Ag\\\u0026rsquo; undez, J. L. Black, H. M. Dunnenberger, G. Ruano, M. A. Kennedy, M. S. Phillips, H. Hachad, T. E. Klein, A. Gaedigk, PharmVar GeneReview: CYP2D6. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 107, 154\u0026ndash;170 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Yang, M. R. Botton, E. R. Scott, S. A. Scott, Sequencing the CYP2D6 gene: from variant allele discovery to clinical pharmacogenetic testing. \u003cem\u003ePharmacogenomics\u003c/em\u003e 18, 673\u0026ndash;685 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW. Kuban, W. A. Daniel, Cytochrome P450 expression and regulation in the brain. \u003cem\u003eDrug Metab. Rev.\u003c/em\u003e 53, 1\u0026ndash;29 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD.-D. Tian, S. Natesan, J. R. White, M. F. Paine, Effects of Common CYP1A2 Genotypes and Other Key Factors on Intraindividual Variation in the Caffeine Metabolic Ratio: An Exploratory Analysis. \u003cem\u003eClin. Transl. Sci.\u003c/em\u003e 12, 39\u0026ndash;46 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. E. Staatz, L. K. Goodman, S. E. Tett, Effect of CYP3A and ABCB1 single nucleotide polymorphisms on the pharmacokinetics and pharmacodynamics of calcineurin inhibitors: Part I. \u003cem\u003eClin. Pharmacokinet.\u003c/em\u003e 49, 141\u0026ndash;175 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. van der Lee, W. J. Rowell, R. Menafra, H.-J. Guchelaar, J. J. Swen, S. Y. Anvar, Application of long-read sequencing to elucidate complex pharmacogenomic regions: a proof of principle. \u003cem\u003ePharmacogenomics J.\u003c/em\u003e 22, 75\u0026ndash;81 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-pharmacogenomics-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tpj","sideBox":"Learn more about [The Pharmacogenomics Journal](http://www.nature.com/tpj/)","snPcode":"41397","submissionUrl":"https://mts-tpj.nature.com/cgi-bin/main.plex","title":"The Pharmacogenomics Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9224585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9224585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSH-SY5Y neuroblastoma cells (SH-SY5Y) are widely used for mechanistic neuropharmacology and toxicity studies, yet their pharmacogenomic background is not explicitly specified. We generated\u0026thinsp;~\u0026thinsp;30\u0026times; PacBio HiFi whole-genome data using the WOBI workflow (Revio), performed QC with FastQC, aligned reads to GRCh38 (pbmm2), and called SNVs/indels (GATK), SVs (pbsv), and CNVs (HiFiCNV). Pharmacogene star-allele diplotypes were assigned using Polygenic with PharmVar-based definitions, complemented by VEP and gnom AD annotations. The selected genes coding for drug-metabolizing enzymes or transporters (\u003cem\u003eCYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, CYP3A5, ABCB1)\u003c/em\u003e, and encoding neurotransmitter receptors, metabolism or transport \u003cem\u003e(DRD2, HTR2A, HTR2C, COMT, SLC6A4)\u003c/em\u003e were investigated. SH-SY5Y carried \u003cem\u003eCYP2C19*17/*17\u003c/em\u003e (ultrarapid metabolism), \u003cem\u003eCYP3A5*3/*3\u003c/em\u003e (non-expresser), CYP2A6*9/*1 (intermediate metabolism), and \u003cem\u003eCYP1A2*1F\u003c/em\u003e with increased \u003cem\u003eCYP1A2\u003c/em\u003e inducibility. Furthermore, a diversity of non-coding regions was found in the studied loci, which may influence gene expression. This long-read reference provides an actionable baseline for designing and interpreting pharmacological experiments with SH-SY5Y.\u003c/p\u003e","manuscriptTitle":"The SH-SY5Y Pharmacogenome Resolved by Long-Read Whole-Genome Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 13:50:55","doi":"10.21203/rs.3.rs-9224585/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-28T08:13:15+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-22T11:22:23+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-07T15:24:52+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-07T09:01:42+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-04-06T18:29:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T16:26:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Pharmacogenomics Journal","date":"2026-03-27T09:44:12+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-03-26T15:35:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T14:44:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-pharmacogenomics-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tpj","sideBox":"Learn more about [The Pharmacogenomics Journal](http://www.nature.com/tpj/)","snPcode":"41397","submissionUrl":"https://mts-tpj.nature.com/cgi-bin/main.plex","title":"The Pharmacogenomics Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"37ccbf1d-e3e4-46c9-88c6-c51900cf1e8d","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65805229,"name":"Biological sciences/Computational biology and bioinformatics/Gene regulatory networks"},{"id":65805230,"name":"Biological sciences/Biotechnology/Sequencing"}],"tags":[],"updatedAt":"2026-04-10T13:50:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 13:50:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9224585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9224585","identity":"rs-9224585","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}