Mapping the Pharmacogenetic Landscape in a Ugandan Population: Implications for Personalized Medicine in an Underrepresented Population.

Africans are extremely underrepresented in global genomic research. African populations face high burdens of communicable and non-communicable diseases and experience widespread polypharmacy. As population-specific genetic studies are crucial to understanding unique genetic profiles and optimizing treatments to reduce medication-related complications in this diverse population, the present study aims to characterize the pharmacogenomics profile of a rural Ugandan population. We analyzed low-pass whole genome sequencing data from 1998 Ugandans to investigate 18 clinically actionable pharmacogenes in this population. We utilized PyPGx to identify star alleles (haplotype patterns) and compared allele frequencies across populations using the Pharmacogenomics Knowledgebase PharmGKB. Clinical interpretations of the identified alleles were conducted following established dosing guidelines. Over 99% of participants displayed actionable phenotypes across the 18 pharmacogenes, averaging 3.5 actionable genotypes per individual. Several variant alleles known to affect drug metabolism (i.e., CYP3A5*1, CYP2B6*9, CYP3A5*6, CYP2D6*17, CYP2D6*29, and TMPT*3C)-which are generally more prevalent in African individuals-were notably enriched in the Ugandan cohort, beyond reported frequencies in other African peoples. More than half of the cohort exhibited a predicted impaired drug response associated with CFTR, IFNL3, CYP2B6, and CYP2C19, and approximately 31% predicted altered CYP2D6 metabolism. Potentially impaired CYP2C9, SLCO1B1, TPMT, and DPYD metabolic phenotypes were also enriched in Ugandans compared with other African populations. Ugandans exhibit distinct allele profiles that could impact drug efficacy and safety. Our findings have important implications for pharmacogenomics in Uganda, particularly with respect to the treatment of prevalent communicable and non-communicable diseases, and they emphasize the potential of pharmacogenomics-guided therapies to optimize healthcare outcomes and precision medicine in Uganda.

Longitudinal Comparative Analysis of Circulating Tumor DNA and Matched Tumor Tissue DNA in Patients with Metastatic Colorectal Cancer Receiving Palliative First-Line Systemic Anti-Cancer Therapy.

Purpose: This study aimed to compare tumor tissue DNA (ttDNA) and circulating tumor DNA (ctDNA) to explore the clinical applicability of ctDNA and to better understand clonal evolution in patients with metastatic colorectal cancer undergoing palliative first-line systemic therapy. Materials and Methods: We performed targeted sequencing analysis of 88 cancer-associated genes using germline DNA, ctDNA at baseline (baseline-ctDNA), and ctDNA at progressive disease (PD-ctDNA). The results were compared with ttDNA data. Results: Among 208 consecutively enrolled patients, we selected 84 (41 males; median age 59, range 35 to 90) with all four sample types available. A total of 202 driver mutations were found in 34 genes. ttDNA exhibited the highest mutation frequency (n=232), followed by baseline-ctDNA (n=155) and PD-ctDNA (n=117). Sequencing ctDNA alongside ttDNA revealed additional mutations in 40 patients (47.6%). PD-ctDNA detected 13 novel mutations in 10 patients (11.9%) compared to ttDNA and baseline-ctDNA. Notably, 7 mutations in 5 patients (6.0%) were missense or nonsense mutations in APC, TP53, SMAD4, and CDH1 genes. In baseline-ctDNA, higher maximal variant allele frequency (VAF) values (p=0.010) and higher VAF values of APC (p=0.012), TP53 (p=0.012), and KRAS (p=0.005) mutations were significantly associated with worse overall survival. Conclusion: While ttDNA remains more sensitive than ctDNA, our ctDNA platform demonstrated validity and potential value when ttDNA was unavailable. Post-treatment analysis of PD-ctDNA unveiled new pathogenic mutations, signifying cancer's clonal evolution. Additionally, baseline-ctDNA's VAF values were prognostic after treatment.

Pharmacogenetic Variation in Neanderthals and Denisovans and Implications for Human Health and Response to Medications.

Modern humans carry both Neanderthal and Denisovan (archaic) genome elements that are part of the human gene pool and affect the life and health of living individuals. The impact of archaic DNA may be particularly evident in pharmacogenes-genes responsible for the processing of exogenous substances such as food, pollutants, and medications-as these can relate to changing environmental effects, and beneficial variants may have been retained as modern humans encountered new environments. However, the health implications and contribution of archaic ancestry in pharmacogenes of modern humans remain understudied. Here, we explore 11 key cytochrome P450 genes (CYP450) involved in 75% of all drug metabolizing reactions in three Neanderthal and one Denisovan individuals and examine archaic introgression in modern human populations. We infer the metabolizing efficiency of these 11 CYP450 genes in archaic individuals and find important predicted phenotypic differences relative to modern human variants. We identify several single nucleotide variants shared between archaic and modern humans in each gene, including some potentially function-altering mutations in archaic CYP450 genes, which may result in altered metabolism in living people carrying these variants. We also identified several variants in the archaic CYP450 genes that are novel and unique to archaic humans as well as one gene, CYP2B6, that shows evidence for a gene duplication found only in Neanderthals and modern Africans. Finally, we highlight CYP2A6, CYP2C9, and CYP2J2, genes which show evidence for archaic introgression into modern humans and posit evolutionary hypotheses that explain their allele frequencies in modern populations.

Comprehensive characterization of maternal, fetal, and neonatal microbiomes supports prenatal colonization of the gastrointestinal tract.

In this study, we aimed to comprehensively characterize the microbiomes of various samples from pregnant women and their neonates, and to explore the similarities and associations between mother-neonate pairs, sample collection sites, and obstetrical factors. We collected samples from vaginal discharge and amniotic fluid in pregnant women and umbilical cord blood, gastric liquid, and meconium from neonates. We identified 19,597,239 bacterial sequences from 641 samples of 141 pregnant women and 178 neonates. By applying rigorous filtering criteria to remove contaminants, we found evidence of microbial colonization in traditionally considered sterile intrauterine environments and the fetal gastrointestinal track. The microbiome distribution was strongly grouped by sample collection site, rather than the mother-neonate pairs. The distinct bacterial composition in meconium, the first stool passed by newborns, supports that microbial colonization occurs during normal pregnancy. The microbiome in neonatal gastric liquid was similar, but not identical, to that in maternal amnionic fluid, as expected since fetuses swallow amnionic fluid in utero and their urine returns to the fluid under normal physiological conditions. Establishing a microbiome library from various samples formed only during pregnancy is crucial for understanding human development and identifying microbiome modifications in obstetrical complications.

Ribosomal DNA replication time coordinates completion of genome replication and anaphase in yeast.

Timely completion of genome replication is a prerequisite for mitosis, genome integrity, and cell survival. A challenge to this timely completion comes from the need to replicate the hundreds of untranscribed copies of rDNA that organisms maintain in addition to the copies required for ribosome biogenesis. Replication of these rDNA arrays is relegated to late S phase despite their large size, repetitive nature, and essentiality. Here, we show that, in Saccharomyces cerevisiae, reducing the number of rDNA repeats leads to early rDNA replication, which results in delaying replication elsewhere in the genome. Moreover, cells with early-replicating rDNA arrays and delayed genome-wide replication aberrantly release the mitotic phosphatase Cdc14 from the nucleolus and enter anaphase prematurely. We propose that rDNA copy number determines the replication time of the rDNA locus and that the release of Cdc14 upon completion of rDNA replication is a signal for cell cycle progression.

PharmVar GeneFocus: SLCO1B1.

The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin-associated musculoskeletal symptoms. To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self-assigned by authors and only loosely followed the star nomenclature system that was first developed for cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator-submitted data were utilized to confirm existing haplotypes, fill knowledge gaps, and/or define novel star alleles. The PharmVar-developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin-associated musculoskeletal symptoms.

ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation.

The accurate identification of genetic variants contributing to therapeutic drug response or adverse effects is the first step in implementation of precision drug therapy. Targeted sequencing has recently become a common methodology for large-scale studies of genetic variation thanks to its favorable balance between low cost, high throughput, and deep coverage. Here, we present ClinPharmSeq, a targeted sequencing panel of 59 genes with associations to pharmacogenetic (PGx) phenotypes, as a platform to explore the relationship between drug response and genetic variation, both common and rare. For validation, we sequenced DNA from 64 ethnically diverse Coriell samples with ClinPharmSeq to call star alleles (haplotype patterns) in 27 genes using the bioinformatics tool PyPGx. These reference samples were extensively characterized by multiple laboratories using PGx testing assays and, more recently, whole genome sequencing. We found that ClinPharmSeq can consistently generate deep-coverage data (mean = 274x) with high uniformity (30x or above = 94.8%). Our genotype analysis identified a total of 185 unique star alleles from sequencing data, and showed that diplotype calls from ClinPharmSeq are highly concordant with that from previous publications (97.6%) and whole genome sequencing (97.9%). Notably, all 19 star alleles with complex structural variation including gene deletions, duplications, and hybrids were recalled with 100% accuracy. Altogether, these results demonstrate that the ClinPharmSeq platform offers a feasible path for broad implementation of PGx testing and optimization of individual drug treatments.

CYP2C8, CYP2C9, and CYP2C19 Characterization Using Next-Generation Sequencing and Haplotype Analysis: A GeT-RM Collaborative Project.

Pharmacogenetic tests typically target selected sequence variants to identify haplotypes that are often defined by star (∗) allele nomenclature. Due to their design, these targeted genotyping assays are unable to detect novel variants that may change the function of the gene product and thereby affect phenotype prediction and patient care. In the current study, 137 DNA samples that were previously characterized by the Genetic Testing Reference Material (GeT-RM) program using a variety of targeted genotyping methods were recharacterized using targeted and whole genome sequencing analysis. Sequence data were analyzed using three genotype calling tools to identify star allele diplotypes for CYP2C8, CYP2C9, and CYP2C19. The genotype calls from next-generation sequencing (NGS) correlated well to those previously reported, except when novel alleles were present in a sample. Six novel alleles and 38 novel suballeles were identified in the three genes due to identification of variants not covered by targeted genotyping assays. In addition, several ambiguous genotype calls from a previous study were resolved using the NGS and/or long-read NGS data. Diplotype calls were mostly consistent between the calling algorithms, although several discrepancies were noted. This study highlights the utility of NGS for pharmacogenetic testing and demonstrates that there are many novel alleles that are yet to be discovered, even in highly characterized genes such as CYP2C9 and CYP2C19.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Transfer learning enables prediction of CYP2D6 haplotype function.

Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in in vitro functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.

Interrogation of CYP2D6 Structural Variant Alleles Improves the Correlation Between CYP2D6 Genotype and CYP2D6-Mediated Metabolic Activity.

The cytochrome P450 2D6 (CYP2D6) gene locus is challenging to accurately genotype due to numerous single nucleotide variants and complex structural variation. Our goal was to determine whether the CYP2D6 genotype-phenotype correlation is improved when diplotype assignments incorporate structural variation, identified by the bioinformatics tool Stargazer, with next-generation sequencing data. Using CYP2D6 activity measured with substrates dextromethorphan and metoprolol, activity score explained 40% and 34% of variability in metabolite formation rates, respectively, when diplotype calls incorporated structural variation, increasing from 36% and 31%, respectively, when diplotypes did not incorporate structural variation. We also investigated whether the revised Clinical Pharmacogenetics Implementation Consortium (CPIC) recommendations for translating genotype to phenotype improve CYP2D6 activity predictions over the current system. Although the revised recommendations do not improve the correlation between activity score and CYP2D6 activity, perhaps because of low frequency of the CYP2D6*10 allele, the correlation with metabolizer phenotype group was significantly improved for both substrates. We also measured the function of seven rare coding variants: one (A449D) exhibited decreased (44%) and another (R474Q) increased (127%) activity compared with reference CYP2D6.1 protein. Allele-specific analysis found that A449D is part of a novel CYP2D6*4 suballele, CYP2D6*4.028. The novel haplotype containing R474Q was designated CYP2D6*138 by PharmVar; another novel haplotype containing R365H was designated CYP2D6*139. Accuracy of CYP2D6 phenotype prediction is improved when the CYP2D6 gene locus is interrogated using next-generation sequencing coupled with structural variation analysis. Additionally, revised CPIC genotype to phenotype translation recommendations provides an improvement in assigning CYP2D6 activity.

Pharmacogenomics of Nicotine Metabolism: Novel CYP2A6 and CYP2B6 Genetic Variation Patterns in Alaska Native and American Indian Populations.

INTRODUCTION: Alaska Native and American Indian (AN/AI) populations have higher tobacco use prevalence than other ethnic/racial groups. Pharmacogenetic testing to tailor tobacco cessation treatment may improve cessation rates. This study characterized polymorphic variations among AN/AI people in genes associated with metabolism of nicotine and drugs used for tobacco cessation. METHODS: Recruitment of AN/AI individuals represented six subgroups, five geographic subgroups throughout Alaska and a subgroup comprised of AIs from the lower 48 states living in Alaska. We sequenced the CYP2A6 and CYP2B6 genes to identify known and novel gain, reduced, and loss-of-function alleles, including structural variation (eg, gene deletions, duplications, and hybridizations). RESULTS: Variant allele frequencies differed substantially between AN/AI subgroups. The gene deletion CYP2A6*4 and reduced function CYP2A6*9 alleles were found at high frequency in Northern/Western subgroups and in Lower 48/Interior subgroups, respectively. The reduced function CYP2B6*6 allele was observed in all subgroups and a novel, predicted reduced function CYP2B6 variant was found at relatively high frequency in the Southeastern subgroup. CONCLUSIONS: Diverse CYP2A6 and CYP2B6 variation among the subgroups highlight the need for comprehensive pharmacogenetic testing to guide tobacco cessation therapy for AN/AI populations. IMPLICATIONS: Nicotine metabolism is largely determined by CYP2A6 genotype, and variation in CYP2A6 activity has altered the treatment success in other populations. These findings suggest pharmacogenetic-guided smoking cessation drug treatment could provide benefit to this unique population seeking tobacco cessation therapy.

Calling Star Alleles With Stargazer in 28 Pharmacogenes With Whole Genome Sequences.

Variation in the enzymatic activity of pharmacogenes is defined by star alleles (haplotypes) comprised of single-nucleotide variants, small insertion-deletions, and large structural variants. We recently developed Stargazer, a next-generation sequencing-based tool to call star alleles for the clinically important CYP2D6 gene. Here, we present the utility of extending Stargazer to call star alleles for 28 pharmacogenes using whole genome sequencing (WGS) data. We applied Stargazer to WGS data from 70 ethnically diverse samples from the Genetic Testing Reference Materials Coordination Program (GeT-RM). These reference samples were extensively characterized by GeT-RM using multiple pharmacogenetic testing assays. In all 28 genes, Stargazer recalled 100% of star alleles (N = 92) present in GeT-RM's consensus genotypes (N = 1,559). Stargazer also detected star alleles not previously reported by GeT-RM, including complex structural variants. Our results demonstrate that combining WGS data and Stargazer enables automated, accurate, and comprehensive genotyping of pharmacogenes in the human genome.

Stargazer: a software tool for calling star alleles from next-generation sequencing data using CYP2D6 as a model.

PURPOSE: Genotyping CYP2D6 is important for precision drug therapy because the enzyme it encodes metabolizes approximately 25% of drugs, and its activity varies considerably among individuals. Genotype analysis of CYP2D6 is challenging due to its highly polymorphic nature. Over 100 haplotypes (star alleles) have been defined for CYP2D6, some involving a gene conversion with its nearby nonfunctional but highly homologous paralog CYP2D7. We present Stargazer, a new bioinformatics tool that uses next-generation sequencing (NGS) data to call star alleles for CYP2D6 ( https://stargazer.gs.washington.edu/stargazerweb/ ). Stargazer is currently being extended for other pharmacogenes. METHODS: Stargazer identifies star alleles from NGS data by detecting single nucleotide variants, insertion-deletion variants, and structural variants. Stargazer detects structural variation, including gene deletions, duplications, and conversions, by calculating paralog-specific copy numbers from read depths. RESULTS: We applied Stargazer to the NGS data of 32 ethnically diverse HapMap trios that were genotyped by TaqMan assays, long-range polymerase chain reaction, quantitative multiplex polymerase chain reaction, high-resolution melting analysis, and/or Sanger sequencing. CYP2D6 genotyping by Stargazer was 99.0% concordant with the data obtained by these methods, and showed that 28.1% of the samples had structural variation including CYP2D6/CYP2D7 hybrids. CONCLUSION: Accurate genotyping of pharmacogenes with NGS and subsequent allele calling with Stargazer will aid the implementation of precision drug therapy.

Hepatic Abundance and Activity of Androgen- and Drug-Metabolizing Enzyme UGT2B17 Are Associated with Genotype, Age, and Sex.

The major objective of this study was to investigate the association of genetic and nongenetic factors with variability in protein abundance and in vitro activity of the androgen-metabolizing enzyme UGT2B17 in human liver microsomes (n = 455). UGT2B17 abundance was quantified by liquid chromatography-tandem mass spectrometry proteomics, and enzyme activity was determined by using testosterone and dihydrotestosterone as in vitro probe substrates. Genotyping or gene resequencing and mRNA expression were also evaluated. Multivariate analysis was used to test the association of UGT2B17 copy number variation, single nucleotide polymorphisms (SNPs), age, and sex with its mRNA expression, abundance, and activity. UGT2B17 gene copy number and SNPs (rs7436962, rs9996186, rs28374627, and rs4860305) were associated with gene expression, protein levels, and androgen glucuronidation rates in a gene dose-dependent manner. UGT2B17 protein (mean ± S.D. picomoles per milligram of microsomal protein) is sparsely expressed in children younger than 9 years (0.12 ± 0.24 years) but profoundly increases from age 9 years to adults (∼10-fold) with ∼2.6-fold greater abundance in males than in females (1.2 vs. 0.47). Association of androgen glucuronidation with UGT2B15 abundance was observed only in the low UGT2B17 expressers. These data can be used to predict variability in the metabolism of UGT2B17 substrates. Drug companies should include UGT2B17 in early phenotyping assays during drug discovery to avoid late clinical failures.