Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease.

Huntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in HTT. The length of this repeat, however, only explains a proportion of the variability in age of onset in patients. Genome-wide association studies have identified modifiers that contribute toward a proportion of the observed variance. By incorporating tissue-specific transcriptomic information with these results, additional modifiers can be identified. We performed a transcriptome-wide association study assessing heritable differences in genetically determined expression in diverse tissues, with genome-wide data from over 4000 patients. Functional validation of prioritized genes was undertaken in isogenic HD stem cells and patient brains. Enrichment analyses were performed with biologically relevant gene sets to identify the core pathways. HD-associated gene coexpression modules were assessed for associations with neurological phenotypes in an independent cohort and to guide drug repurposing analyses. Transcriptomic analyses identified genes that were associated with age of HD onset and displayed colocalization with gene expression signals in brain tissue (FAN1, GPR161, PMS2, SUMF2), with supporting evidence from functional experiments. This included genes involved in DNA repair, as well as novel-candidate modifier genes that have been associated with other neurological conditions. Further, cortical coexpression modules were also associated with cognitive decline and HD-related traits in a longitudinal cohort. In summary, the combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder. Further, these analyses expanded the pathways potentially involved in modifying HD onset and prioritized candidate therapeutics for future study.

Length of Uninterrupted CAG, Independent of Polyglutamine Size, Results in Increased Somatic Instability, Hastening Onset of Huntington Disease.

Huntington disease (HD) is caused by a CAG repeat expansion in the huntingtin (HTT) gene. Although the length of this repeat is inversely correlated with age of onset (AOO), it does not fully explain the variability in AOO. We assessed the sequence downstream of the CAG repeat in HTT [reference: (CAG)n-CAA-CAG], since variants within this region have been previously described, but no study of AOO has been performed. These analyses identified a variant that results in complete loss of interrupting (LOI) adenine nucleotides in this region [(CAG)n-CAG-CAG]. Analysis of multiple HD pedigrees showed that this LOI variant is associated with dramatically earlier AOO (average of 25 years) despite the same polyglutamine length as in individuals with the interrupting penultimate CAA codon. This LOI allele is particularly frequent in persons with reduced penetrance alleles who manifest with HD and increases the likelihood of presenting clinically with HD with a CAG of 36-39 repeats. Further, we show that the LOI variant is associated with increased somatic repeat instability, highlighting this as a significant driver of this effect. These findings indicate that the number of uninterrupted CAG repeats, which is lengthened by the LOI, is the most significant contributor to AOO of HD and is more significant than polyglutamine length, which is not altered in these individuals. In addition, we identified another variant in this region, where the CAA-CAG sequence is duplicated, which was associated with later AOO. Identification of these cis-acting modifiers have potentially important implications for genetic counselling in HD-affected families.

Glutaminase Deficiency Caused by Short Tandem Repeat Expansion in GLS.

We report an inborn error of metabolism caused by an expansion of a GCA-repeat tract in the 5' untranslated region of the gene encoding glutaminase (GLS) that was identified through detailed clinical and biochemical phenotyping, combined with whole-genome sequencing. The expansion was observed in three unrelated patients who presented with an early-onset delay in overall development, progressive ataxia, and elevated levels of glutamine. In addition to ataxia, one patient also showed cerebellar atrophy. The expansion was associated with a relative deficiency of GLS messenger RNA transcribed from the expanded allele, which probably resulted from repeat-mediated chromatin changes upstream of the GLS repeat. Our discovery underscores the importance of careful examination of regions of the genome that are typically excluded from or poorly captured by exome sequencing.

Patient-specific genetic factors predict treatment failure in sofosbuvir-treated patients with chronic hepatitis C.

BACKGROUND & AIMS: According to pivotal clinical trials, cure rates for sofosbuvir-based antiviral therapy exceed 96%. Treatment failure is usually assumed to be because of virological resistance-associated substitutions or clinical risk factors, yet the role of patient-specific genetic factors has not been well explored. We determined if patient-specific genetic factors help predict patients likely to fail sofosbuvir treatment in real-world treatment situations. METHODS: We recruited sofosbuvir-treated patients with chronic hepatitis C from five Canadian treatment sites, and performed a case-control pharmacogenomics study assessing both previously published and novel genetic polymorphisms. Specifically studied were variants predicted to impair CES1-dependent production of sofosbuvir's active metabolite, interferon-λ signalling variants expected to impact a patient's immune response to the virus and an HLA variant associated with increased spontaneous and treatment-induced viral clearance. RESULTS: Three hundred and fifty-nine sofosbuvir-treated patients were available for analyses after exclusions, with 34 (9.5%) failing treatment. We identified CES1 variants as novel predictors for treatment failure in European patients (rs115629050 or rs4513095; odds ratio (OR): 5.43; 95% confidence interval (CI): 1.64-18.01; P = .0057), replicated associations with IFNL4 variants predicted to increase interferon-λ signalling (eg rs12979860; OR: 2.25; 95% CI: 1.25-4.06; P = .0071) and discovered a novel association with a coding variant predicted to enhance the activity of IFNL4's receptor (rs2834167 in IL10RB; OR: 1.81; 95% CI: 1.01-3.24; P = .047). CONCLUSIONS: Ultimately, this work demonstrates that patient-specific genetic factors could be used as a tool to identify patients at higher risk of treatment failure and allow for these patients to receive effective therapy sooner.

A cross-sectional study of the relationship between CYP2D6 and CYP2C19 variations and depression symptoms, for women taking SSRIs during pregnancy.

Depression during pregnancy affects 10-15% of women, and 5% of women take antidepressants during pregnancy. Clinical guidelines provide recommendations for selective serotonin reuptake inhibitor (SSRI) drug choice and dose based on CYP2D6 and CYP2C19 genotype; however, they are based on evidence from non-pregnant cohorts. This study aimed to test the hypothesis that women with function-altering variants (increased, decreased, or no function) in these pharmacogenes, taking SSRIs prenatally, would have more depression symptoms than women whose pharmacogenetic variants are associated with normal SSRI metabolism. Comprehensive CYP2D6 and CYP2C19 genotyping using a range of methods, including gene copy number analysis, was performed as secondary analyses on two longitudinal cohorts of pregnant women (N = 83) taking the SSRIs paroxetine, citalopram, escitalopram, or sertraline. The Kruskal-Wallis test compared mean depression scores across four predicted metabolizer groups: poor (n = 5), intermediate (n = 10), normal (n = 53), and ultrarapid (n = 15). There were no significant differences between mean depression scores across the four metabolizer groups (H(3) = .73, p = .87, eta-squared = .029, epsilon-squared = .0089). This is the first study of the relationship in pregnancy between CYP2C19 pharmacogenetic variations and depression symptoms in the context of SSRI use. Findings from this initial study do not support the clinical use of pharmacogenetic testing for SSRI use during the second or third trimesters of pregnancy, but these findings should be confirmed in larger cohorts. There is an urgent need for further research to clarify the utility of pharmacogenetic testing for pregnant women, especially as companies offering direct-to-consumer genetic testing expand their marketing efforts.

Frequency of the loss of CAA interruption in the HTT CAG tract and implications for Huntington disease in the reduced penetrance range.

PURPOSE: In some Huntington disease (HD) patients, the "loss of interruption" (LOI) variant eliminates an interrupting codon in the HTT CAG-repeat tract, which causes earlier age of onset (AOO). The magnitude of this effect is uncertain, since previous studies included few LOI carriers, and the variant also causes CAG size misestimation. We developed a rapid LOI detection screen, enabling unbiased frequency estimation among manifest HD patients. Additionally, we combined published data with clinical data from newly identified patients to accurately characterize the LOI's effect on AOO. METHODS: We developed a LOI detection polymerase chain reaction (PCR) assay, and screened patients to estimate the frequency of the LOI variant and its effect on AOO. RESULTS: Mean onset for LOI carriers (n = 49) is 20.4 years earlier than expected based on diagnosed CAG size. After correcting for CAG size underestimation, the variant is still associated with onset 9.5 years earlier. The LOI is present in 1.02% of symptomatic HD patients, and in 32.2% of symptomatic reduced penetrance (RP) range patients (36-39 CAGs). CONCLUSION: The LOI causes significantly earlier onset, greater than expected by CAG length, particularly in persons with 36-39 CAG repeats. Detection of this variant has implications for HD families, especially for those in the RP range.

CYP2D6 as a treatment decision aid for ER-positive non-metastatic breast cancer patients: a systematic review with accompanying clinical practice guidelines.

PURPOSE: Tamoxifen is one of the principal treatments for estrogen receptor (ER)-positive breast cancer. Unfortunately, between 30 and 50% of patients receiving this hormonal therapy relapse. Since CYP2D6 genetic variants have been reported to play an important role in survival outcomes after treatment with tamoxifen, this study sought to summarize and critically appraise the available scientific evidence on this topic. METHODS: A systematic literature review was conducted to identify studies investigating associations between CYP2D6 genetic variation and survival outcomes after tamoxifen treatment. Critical appraisal of the retrieved scientific evidence was performed, and recommendations were developed for CYP2D6 genetic testing in the context of tamoxifen therapy. RESULTS: Although conflicting literature exists, the majority of the current evidence points toward CYP2D6 genetic variation affecting survival outcomes after tamoxifen treatment. Of note, review of the CYP2D6 genotyping assays used in each of the studies revealed the importance of comprehensive genotyping strategies to accurately predict CYP2D6 metabolizer phenotypes. CONCLUSIONS AND RECOMMENDATIONS: Critical appraisal of the literature provided evidence for the value of comprehensive CYP2D6 genotyping panels in guiding treatment decisions for non-metastatic ER-positive breast cancer patients. Based on this information, it is recommended that alternatives to standard tamoxifen treatments may be considered in CYP2D6 poor or intermediate metabolizers.

The molecular epidemiology of Huntington disease is related to intermediate allele frequency and haplotype in the general population.

Huntington disease (HD) is the most common monogenic neurodegenerative disorder in populations of European ancestry, but occurs at lower prevalence in populations of East Asian or black African descent. New mutations for HD result from CAG repeat expansions of intermediate alleles (IAs), usually of paternal origin. The differing prevalence of HD may be related to the rate of new mutations in a population, but no comparative estimates of IA frequency or the HD new mutation rate are available. In this study, we characterize IA frequency and the CAG repeat distribution in fifteen populations of diverse ethnic origin. We estimate the HD new mutation rate in a series of populations using molecular IA expansion rates. The frequency of IAs was highest in Hispanic Americans and Northern Europeans, and lowest in black Africans and East Asians. The prevalence of HD correlated with the frequency of IAs by population and with the proportion of IAs found on the HD-associated A1 haplotype. The HD new mutation rate was estimated to be highest in populations with the highest frequency of IAs. In European ancestry populations, one in 5,372 individuals from the general population and 7.1% of individuals with an expanded CAG repeat in the HD range are estimated to have a molecular new mutation. Our data suggest that the new mutation rate for HD varies substantially between populations, and that IA frequency and haplotype are closely linked to observed epidemiological differences in the prevalence of HD across major ancestry groups in different countries.

The identification of novel genetic variants associated with antipsychotic treatment response outcomes in first-episode schizophrenia patients.

BACKGROUND: Although antipsychotics are integral to the treatment of schizophrenia, drug efficacy varies between patients. Although it has been shown that antipsychotic treatment response outcomes are heritable, our understanding of the genetic factors that are involved remains incomplete. Therefore, this study aims to use an unbiased scan of the genome to identify the genetic variants contributing toward antipsychotic treatment response outcomes. MATERIALS AND METHODS: This study utilized whole-exome sequencing of patients on extreme ends of the treatment response spectrum (n=11) in combination with results from previous antipsychotic studies to design a panel of variants that were genotyped in two well-characterized first-episode schizophrenia cohorts (n=103 and 87). Association analyses were carried out to determine whether these variants were significantly associated with antipsychotic treatment response outcomes. RESULTS: Association analyses in the discovery cohort identified two nonsynonymous variants that were significantly associated with antipsychotic treatment response outcomes (P<2.7 × 10(-5)), which were also significantly associated with the corresponding treatment response outcome in an independent replication cohort. Computational approaches showed that both of these nonsynonymous variants--rs13025959 in MYO7B (E1647D) and rs10380 in MTRR (H622Y)--were predicted to impair the functioning of their corresponding protein products. CONCLUSION: The use of whole-exome sequencing in a subset of patients from a well-characterized cohort of first-episode schizophrenia patients, for whom longitudinal depot treatment response data were available, allowed for (i) the removal of confounding factors related to treatment progression and compliance and (ii) the identification of two genetic variants that have not been associated previously with antipsychotic treatment response outcomes and whose results were applicable across different classes of antipsychotics. Although the genes that are affected by these variants are involved in pathways that have been related previously to antipsychotic treatment outcomes, the identification of these novel genes will play an important role in improving our understanding of the specific variants involved in antipsychotic treatment response outcomes.

Informed consent and ethical re-use of African genomic data.

Rapid advances in human genomic research are increasing the availability of genomic data for secondary analysis. Particularly in the case of vulnerable African populations, ethics and informed consent processes need to be transparent--both to ensure participant protection, as well as to share skills and to evolve best practice for informed consent from a shared knowledge base. An open dialogue between all stakeholders can facilitate this.

Characterization of CYP2B6 and CYP2A6 Pharmacogenetic Variation in Sub-Saharan African Populations.

Genetic variation in CYP2B6 and CYP2A6 is known to impact interindividual response to antiretrovirals, nicotine, and bupropion, among other drugs. However, the full catalogue of clinically relevant pharmacogenetic variants in these genes is yet to be established, especially across African populations. This study therefore aimed to characterize the star allele (haplotype) distribution in CYP2B6 and CYP2A6 across diverse and understudied sub-Saharan African (SSA) populations. We called star alleles from 961 high-depth full genomes using StellarPGx, Aldy, and PyPGx. In addition, we performed CYP2B6 and CYP2A6 star allele frequency comparisons between SSA and other global biogeographical groups represented in the new 1000 Genomes Project high-coverage dataset (n = 2,000). This study presents frequency information for star alleles in CYP2B6 (e.g., *6 and *18; frequency of 21-47% and 2-19%, respectively) and CYP2A6 (e.g., *4, *9, and *17; frequency of 0-6%, 3-10%, and 6-20%, respectively), and predicted phenotypes (for CYP2B6), across various African populations. In addition, 50 potentially novel African-ancestry star alleles were computationally predicted by StellarPGx in CYP2B6 and CYP2A6 combined. For each of these genes, over 4% of the study participants had predicted novel star alleles. Three novel star alleles in CYP2A6 (*54, *55, and *56) and CYP2B6 apiece, and several suballeles were further validated via targeted Single-Molecule Real-Time resequencing. Our findings are important for informing the design of comprehensive pharmacogenetic testing platforms, and are highly relevant for personalized medicine strategies, especially relating to antiretroviral medication and smoking cessation treatment in Africa and the African diaspora. More broadly, this study highlights the importance of sampling diverse African ethnolinguistic groups for accurate characterization of the pharmacogene variation landscape across the continent.

Next-generation sequencing of pharmacogenes: a critical analysis focusing on schizophrenia treatment.

INTRODUCTION: Because of the unmet needs of current pharmacotherapy for schizophrenia, antipsychotic pharmacogenetic research is of utmost importance. However, to date, few clinically applicable antipsychotic pharmacogenomic alleles have been identified. Nonetheless, next-generation sequencing technologies are expected to aid in the identification of clinically significant variants for this complex phenotype. The aim of this study was therefore to critically examine the ability of next-generation sequencing technologies to reliably detect variation present in pharmacogenes. MATERIALS AND METHODS: Candidate antipsychotic pharmacogenes and very important pharmacogenes were identified from the literature and the Pharmacogenomics Knowledgebase. Thereafter, the percentage sequence similarity observed between these genes and their corresponding pseudogenes and paralogues, as well as the percentage low-complexity sequence and GC content of each gene, was calculated. These sequence attributes were subsequently compared with the 'inaccessible' regions of these genes as described by the 1000 Genomes Project. RESULTS: It was found that the percentage 'inaccessible genome' correlated well with GC content (P=9.96×10), low-complexity sequence (P=0.0002) and the presence of pseudogenes/paralogues (P=8.02×10). In addition, it was found that many of the pharmacogenes were not ideally suited to next-generation sequencing because of these genomic complexities. These included the CYP and HLA genes, both of which are of importance to many fields of pharmacogenetics. CONCLUSION: Current short read sequencing technologies are unable to comprehensively capture the variation in all pharmacogenes. Therefore, until high-throughput sequencing technologies advance further, it may be necessary to combine next-generation sequencing with other genotyping strategies.

Introduction of the AmpliChip CYP450 Test to a South African cohort: a platform comparative prospective cohort study.

BACKGROUND: Adverse drug reactions and lack of therapeutic efficacy associated with currently prescribed pharmacotherapeutics may be attributed, in part, to inter-individual variability in drug metabolism. Studies on the pharmacogenetics of Cytochrome P450 (CYP) enzymes offer insight into this variability. The objective of this study was to compare the AmpliChip CYP450 Test® (AmpliChip) to alternative genotyping platforms for phenotype prediction of CYP2C19 and CYP2D6 in a representative cohort of the South African population. METHODS: AmpliChip was used to screen for thirty-three CYP2D6 and three CYP2C19 alleles in two different cohorts. As a comparison cohort 2 was then genotyped using a CYP2D6 specific long range PCR with sequencing (CYP2D6 XL-PCR + Sequencing) platform and a PCR-RFLP platform for seven CYP2C19 alleles. RESULTS: Even though there was a low success rate for the AmpliChip, allele frequencies for both CYP2D6 and CYP2C19 were very similar between the two different cohorts. The CYP2D6 XL-PCR + Sequencing platform detected CYP2D6*5 more reliably and could correctly distinguish between CYP2D6*2 and *41 in the Black African individuals. Alleles not covered by the AmpliChip were identified and four novel CYP2D6 alleles were also detected. CYP2C19 PCR-RFLP identified CYP2C19*9,*15, *17 and *27 in the Black African individuals, with *2, *17 and *27 being relatively frequent in the cohort. Eliminating mismatches and identifying additional alleles will contribute to improving phenotype prediction for both enzymes. Phenotype prediction differed between platforms for both genes. CONCLUSION: Comprehensive genotyping of CYP2D6 and CYP2C19 with the platforms used in this study, would be more appropriate than AmpliChip for phenotypic prediction in the South African population. Pharmacogenetically important novel alleles may remain undiscovered when using assays that are designed according to Caucasian specific variation, unless alternate strategies are utilised.

Ethical and legal implications of whole genome and whole exome sequencing in African populations.

BACKGROUND: Rapid advances in high throughput genomic technologies and next generation sequencing are making medical genomic research more readily accessible and affordable, including the sequencing of patient and control whole genomes and exomes in order to elucidate genetic factors underlying disease. Over the next five years, the Human Heredity and Health in Africa (H3Africa) Initiative, funded by the Wellcome Trust (United Kingdom) and the National Institutes of Health (United States of America), will contribute greatly towards sequencing of numerous African samples for biomedical research. DISCUSSION: Funding agencies and journals often require submission of genomic data from research participants to databases that allow open or controlled data access for all investigators. Access to such genotype-phenotype and pedigree data, however, needs careful control in order to prevent identification of individuals or families. This is particularly the case in Africa, where many researchers and their patients are inexperienced in the ethical issues accompanying whole genome and exome research; and where an historical unidirectional flow of samples and data out of Africa has created a sense of exploitation and distrust. In the current study, we analysed the implications of the anticipated surge of next generation sequencing data in Africa and the subsequent data sharing concepts on the protection of privacy of research subjects. We performed a retrospective analysis of the informed consent process for the continent and the rest-of-the-world and examined relevant legislation, both current and proposed. We investigated the following issues: (i) informed consent, including guidelines for performing culturally-sensitive next generation sequencing research in Africa and availability of suitable informed consent documents; (ii) data security and subject privacy whilst practicing data sharing; (iii) conveying the implications of such concepts to research participants in resource limited settings. SUMMARY: We conclude that, in order to meet the unique requirements of performing next generation sequencing-related research in African populations, novel approaches to the informed consent process are required. This will help to avoid infringement of privacy of individual subjects as well as to ensure that informed consent adheres to acceptable data protection levels with regard to use and transfer of such information.

Characterization of CYP2D6 Pharmacogenetic Variation in Sub-Saharan African Populations.

Cytochrome P450 2D6 (CYP2D6) is a key enzyme in drug response owing to its involvement in the metabolism of ~ 25% of clinically prescribed medications. The encoding CYP2D6 gene is highly polymorphic, and many pharmacogenetics studies have been performed worldwide to investigate the distribution of CYP2D6 star alleles (haplotypes); however, African populations have been relatively understudied to date. In this study, the distributions of CYP2D6 star alleles and predicted drug metabolizer phenotypes-derived from activity scores-were examined across multiple sub-Saharan African populations based on bioinformatics analysis of 961 high-depth whole genome sequences. This was followed by characterization of novel star alleles and suballeles in a subset of the participants via targeted high-fidelity Single-Molecule Real-Time resequencing (Pacific Biosciences). This study revealed varying frequencies of known CYP2D6 alleles and predicted phenotypes across different African ethnolinguistic groups. Twenty-seven novel CYP2D6 star alleles were predicted computationally and two of them were further validated. This study highlights the importance of studying variation in key pharmacogenes such as CYP2D6 in the African context to better understand population-specific allele frequencies. This will aid in the development of better genotyping panels and star allele detection approaches with a view toward supporting effective implementation of precision medicine strategies in Africa and across the African diaspora.

A Systematic Review and Analysis of the Use of Polygenic Scores in Pharmacogenomics.

Polygenic scores (PGSs) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGSs for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n = 30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGSs were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g., schizophrenia-derived PGSs for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGSs, with between 3 and 6.6 million variants included in the PGSs. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGSs deposited on the Polygenic Score Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.

A pharmacogenomic investigation of the cardiac safety profile of ondansetron in children and pregnant women.

BACKGROUND: Ondansetron is a highly effective antiemetic for the treatment of nausea and vomiting. However, this medication has also been associated with QT prolongation. Pharmacogenomic information on therapeutic response to ondansetron exists, but no investigation has been performed on genetic factors that influence the cardiac safety of this medication. METHODS: Three patient groups receiving ondansetron were recruited and followed prospectively (pediatric post-surgical patients n = 101; pediatric oncology patients n = 98; pregnant women n = 62). Electrocardiograms were conducted at baseline, and 5- and 30-min post-ondansetron administration, to determine the effect of ondansetron treatment on QT interval. Pharmacogenomic associations were assessed via analyses of comprehensive CYP2D6 genotyping and genome-wide association study data. RESULTS: In the entire cohort, 62 patients (24.1%) met the criteria for prolonged QT, with 1.2% of the cohort exhibiting unsafe QT prolongation. The most significant shift from baseline occurred at five minutes post-ondansetron administration (P = 9.8 × 10-4). CYP2D6 activity score was not associated with prolonged QT. Genome-wide analyses identified novel associations with a missense variant in TLR3 (rs3775291; P = 2.00 × 10-7) and a variant linked to the expression of SLC36A1 (rs34124313; P = 1.97 × 10-7). CONCLUSIONS: This study has provided insight into the genomic basis of ondansetron-induced cardiac changes and has emphasized the importance of genes that have been implicated in serotonin-related traits. These biologically-relevant findings represent the first step towards understanding this adverse event with the overall goal to improve the safety of this commonly used antiemetic medication.

StellarPGx: A Nextflow Pipeline for Calling Star Alleles in Cytochrome P450 Genes.

Bioinformatics pipelines for calling star alleles (haplotypes) in cytochrome P450 (CYP) genes are important for the implementation of precision medicine. Genotyping CYP genes using high throughput sequencing data is complicated, e.g., by being highly polymorphic, not to mention the structural variations especially in CYP2D6, CYP2A6, and CYP2B6. Genome graph-based variant detection approaches have been shown to be reliable for genotyping HLA alleles. However, their application to enhancing star allele calling in CYP genes has not been extensively explored. We present StellarPGx, a Nextflow pipeline for accurately genotyping CYP genes by combining genome graph-based variant detection, read coverage information from the original reference-based alignments, and combinatorial diplotype assignments. The implementation of StellarPGx using Nextflow facilitates its portability, reproducibility, and scalability on various user platforms. StellarPGx is currently able to genotype 12 important pharmacogenes belonging to the CYP1, 2, and 3 families. For purposes of validation, we use CYP2D6 as a model gene owing to its high degree of polymorphisms (over 130 star alleles defined to date, including complex structural variants) and clinical importance. We applied StellarPGx and three existing callers to 109 whole genome sequenced samples for which the Genetic Testing Reference Material Coordination Program (GeT-RM) has recently provided consensus truth CYP2D6 diplotypes. StellarPGx had the highest CYP2D6 diplotype concordance (99%) with GeT-RM compared with Cyrius (98%), Aldy (82%), and Stargazer (84%). This exemplifies the high accuracy of StellarPGx and highlights its importance for both research and clinical pharmacogenomics applications. The StellarPGx pipeline is open-source and available from https://github.com/SBIMB/StellarPGx.

Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury.

Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the "one-treatment fits all" approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex "-omics" data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.