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.

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 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.

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.

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.

The Extent and Impact of Variation in ADME Genes in Sub-Saharan African Populations.

Introduction: Investigating variation in genes involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs are key to characterizing pharmacogenomic (PGx) relationships. ADME gene variation is relatively well characterized in European and Asian populations, but data from African populations are under-studied-which has implications for drug safety and effective use in Africa. Results: We identified significant ADME gene variation in African populations using data from 458 high-coverage whole genome sequences, 412 of which are novel, and from previously available African sequences from the 1,000 Genomes Project. ADME variation was not uniform across African populations, particularly within high impact coding variation. Copy number variation was detected in 116 ADME genes, with equal ratios of duplications/deletions. We identified 930 potential high impact coding variants, of which most are discrete to a single African population cluster. Large frequency differences (i.e., >10%) were seen in common high impact variants between clusters. Several novel variants are predicted to have a significant impact on protein structure, but additional functional work is needed to confirm the outcome of these for PGx use. Most variants of known clinical outcome are rare in Africa compared to European populations, potentially reflecting a clinical PGx research bias to European populations. Discussion: The genetic diversity of ADME genes across sub-Saharan African populations is large. The Southern African population cluster is most distinct from that of far West Africa. PGx strategies based on European variants will be of limited use in African populations. Although established variants are important, PGx must take into account the full range of African variation. This work urges further characterization of variants in African populations including in vitro and in silico studies, and to consider the unique African ADME landscape when developing precision medicine guidelines and tools for African populations.

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.

Novel variant in glycophorin c gene protects against ribavirin-induced anemia during chronic hepatitis C treatment.

BACKGROUND: The current use of ribavirin in difficult-to-cure chronic hepatitis C patients (HCV) and patients with severe respiratory infections is constrained by the issue of ribavirin-induced hemolytic anemia that affects 30% of treated patients, requiring dosage modification or discontinuation. Though some genetic variants have been identified predicting this adverse effect, known clinical and genetic factors do not entirely explain the risk of ribavirin-induced anemia. METHODS: We assessed the associations of previously identified variants in inosine triphosphatase (ITPA), solute carrier 28A2 (SLC28A2) and vitamin D receptor (VDR) genes with ribavirin-induced anemia defined as hemoglobin decline of ≥30 g/L on treatment, followed by a staged discovery (n = 114), replication (n = 74), and combined (n = 188) genome-wide association study to uncover potential new predictive variants. RESULTS: We identified a novel association in the gene coding glycophorin C (rs6741425; OR:0.12, 95%CI:0.04-0.34, P = 2.94 × 10-6) that predicts protection against ribavirin-induced anemia. We also replicated the associations of ITPA and VDR genetic variants with the development of ribavirin-induced anemia (rs1127354; OR:0.13, 95%CI:0.04-0.41, P = 8.66 ×10-5; and rs1544410; OR:1.65, 95%CI:1.01-2.70, P = 0.0437). CONCLUSIONS: GYPC variation affecting erythrocyte membrane strength is important in predicting risk for developing ribavirin-induced anemia. ITPA and VDR genetic variants are also important predictors of this adverse reaction.

Transcriptome-wide association study uncovers the role of essential genes in anthracycline-induced cardiotoxicity.

Anthracyclines are highly effective chemotherapeutic agents; however, their clinical utility is limited by severe anthracycline-induced cardiotoxicity (ACT). Genome-wide association studies (GWAS) have uncovered several genetic variants associated with ACT, but the impact of these findings requires further elucidation. We conducted a transcriptome-wide association study (TWAS) using our previous GWAS summary statistics (n = 280 patients) to identify gene expression-related associations with ACT. We identified a genetic association between decreased expression of GDF5 and ACT (Z-score = -4.30, P = 1.70 × 10-5), which was replicated in an independent cohort (n = 845 patients, P = 3.54 × 10-3). Additionally, cell viability of GDF5-silenced human cardiac myocytes was significantly decreased in response to anthracycline treatment. Subsequent gene set enrichment and pathway analyses of the TWAS data revealed that genes essential for survival, cardioprotection and response to anthracyclines, as well as genes involved in ribosomal, spliceosomal and cardiomyopathy pathways are important for the development of ACT.

Integrating disease and drug-related phenotypes for improved identification of pharmacogenomic variants.

Aim: To improve the identification and interpretation of pharmacogenetic variants through the integration of disease and drug-related traits. Materials & methods: We hypothesized that integrating genome-wide disease and pharmacogenomic data may drive new insights into drug toxicity and response by identifying shared genetic architecture. Pleiotropic variants were identified using a methodological framework incorporating colocalization analysis. Results: Using genome-wide association studies summary statistics from the UK Biobank, European Bioinformatics Institute genome-wide association studies catalog and the Pharmacogenomics Research Network, we validated pleiotropy at the ABCG2 locus between allopurinol response and gout and identified novel pleiotropy between antihypertensive-induced new-onset diabetes, Crohn's disease and inflammatory bowel disease at the IL18RAP/SLC9A4 locus. Conclusion: New mechanistic insights and genetic loci can be uncovered by identifying pleiotropy between disease and drug-related traits.

Lay abstract Disease-focused genome-wide association studies (GWAS) have identified a plethora of actionable genetic variants over the last 20 years. International collaboration and technological breakthroughs have enabled rapid genotyping and data collection, which has correspondingly increased sample size and power. Contrastingly, recruitment of well-characterized cohorts of patients for pharmacogenomics research has proven challenging. Given the greater number of associated genetic variants and larger cohort sizes in common disease GWAS, we hypothesized that integrating genome-wide disease and pharmacogenomic data may drive new insights into drug toxicity and drug efficacy phenotypes, beyond the standard scope of current pharmacogenetic analyses. Using GWAS summary statistics from the UK Biobank, European Bioinformatics Institute GWAS catalog, and the Pharmacogenomics Research Network, and a methodological framework incorporating colocalization analysis, we validated pleiotropy at the ABCG2 locus between allopurinol response, gout, and serum urate and identified novel pleiotropy between antihypertensive-induced new-onset diabetes, Crohn's disease and inflammatory bowel disease at the IL18RAP/SLC9A4 locus.

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.

Interrupting sequence variants and age of onset in Huntington's disease: clinical implications and emerging therapies.

BACKGROUND: Huntington's disease is a fatal neurodegenerative disorder that is caused by CAG-CAA repeat expansion, encoding polyglutamine, in the huntingtin (HTT) gene. Current age-of-clinical-onset prediction models for Huntington's disease are based on polyglutamine length and explain only a proportion of the variability in age of onset observed between patients. These length-based assays do not interrogate the underlying genetic variation, because known genetic variants in this region do not alter the protein coding sequence. Given that individuals with identical repeat lengths can present with Huntington's disease decades apart, the search for genetic modifiers of clinical age of onset has become an active area of research. RECENT DEVELOPMENTS: Results from three independent genetic studies of Huntington's disease have shown that glutamine-encoding CAA variants that interrupt DNA CAG repeat tracts, but do not alter polyglutamine length or polyglutamine homogeneity, are associated with substantial differences in age of onset of Huntington's disease in carriers. A variant that results in the loss of CAA interruption is associated with early onset and is particularly relevant to individuals that carry alleles in the reduced penetrance range (ie, CAG 36-39). Approximately a third of clinically manifesting carriers of reduced penetrance alleles, defined by current diagnostics, carry this variant. Somatic repeat instability, modified by interrupted CAG tracts, is the most probable cause mediating this effect. This relationship is supported by genome-wide screens for disease modifiers, which have revealed the importance of DNA-repair genes in Huntington's disease (ie, FAN1, LIG1, MLH1, MSH3, PMS1, and PMS2). WHERE NEXT?: Focus needs to be placed on refining our understanding of the effect of the loss-of-interruption and duplication-of-interruption variants and other interrupting sequence variants on age of onset, and assessing their effect in disease-relevant brain tissues, as well as in diverse population groups, such as individuals from Africa and Asia. Diagnostic tests should be augmented or updated, since current tests do not assess the underlying DNA sequence variation, especially when assessing individuals that carry alleles in the reduced penetrance range. Future studies should explore somatic repeat instability and DNA repair as new therapeutic targets to modify age of onset in Huntington's disease and in other repeat-mediated disorders. Disease-modifying therapies could potentially be developed by therapeutically targeting these processes. Promising approaches include therapeutically targeting the expanded repeat or directly perturbing key DNA-repair genes (eg, with antisense oligonucleotides or small molecules). Targeting the CAG repeat directly with naphthyridine-azaquinolone, a compound that induces contractions, and altering the expression of MSH3, represent two viable therapeutic strategies. However, as a first step, the capability of such novel therapeutic approaches to delay clinical onset in animal models should be assessed.

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.

A systematic comparison of pharmacogene star allele calling bioinformatics algorithms: a focus on CYP2D6 genotyping.

Genetic variation in genes encoding cytochrome P450 enzymes has important clinical implications for drug metabolism. Bioinformatics algorithms for genotyping these highly polymorphic genes using high-throughput sequence data and automating phenotype prediction have recently been developed. The CYP2D6 gene is often used as a model during the validation of these algorithms due to its clinical importance, high polymorphism, and structural variations. However, the validation process is often limited to common star alleles due to scarcity of reference datasets. In addition, there has been no comprehensive benchmark of these algorithms to date. We performed a systematic comparison of three star allele calling algorithms using 4618 simulations as well as 75 whole-genome sequence samples from the GeT-RM project. Overall, we found that Aldy and Astrolabe are better suited to call both common and rare diplotypes compared to Stargazer, which is affected by population structure. Aldy was the best performing algorithm in calling CYP2D6 structural variants followed by Stargazer, whereas Astrolabe had limitations especially in calling hybrid rearrangements. We found that ensemble genotyping, characterised by taking a consensus of genotypes called by all three algorithms, has higher haplotype concordance but it is prone to ambiguities whenever complete discrepancies between the tools arise. Further, we evaluated the effects of sequencing coverage and indel misalignment on genotyping accuracy. Our account of the strengths and limitations of these algorithms is extremely important to clinicians and researchers in the pharmacogenomics and precision medicine communities looking to haplotype CYP2D6 and other pharmacogenes using high-throughput sequencing data.

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.

Outcome Definition Influences the Relationship Between Genetic Polymorphisms of ERCC1, ERCC2, SLC22A2 and Cisplatin Nephrotoxicity in Adult Testicular Cancer Patients.

Although previous research identified candidate genetic polymorphisms associated with cisplatin nephrotoxicity, varying outcome definitions potentially contributed to the variability in the effect size and direction of this relationship. We selected genetic variants that have been significantly associated with cisplatin-induced nephrotoxicity in more than one published study (SLC22A2 rs316019; ERCC1 rs11615 and rs3212986; ERCC2 rs1799793 and rs13181) and performed a replication analysis to confirm associations between these genetic polymorphisms and cisplatin nephrotoxicity using various outcome definitions. We included 282 germ cell testicular cancer patients treated with cisplatin from 2009-2014, aged >17 years recruited by the Canadian Pharmacogenomics Network for Drug Safety. Nephrotoxicity was defined using four grading tools: (1) Common Terminology Criteria for Adverse Events (CTCAE) v4.03 for acute kidney injury (AKI) or CTCAE-AKI; (2) adjusted cisplatin-induced AKI; (3) elevation of serum creatinine; and (4) reduction in the estimated glomerular filtration rate (eGFR). Significant associations were only found when using the CTCAE v4.03 definition: genotype CA of the ERCC1 rs3212986 was associated with decreased risk of cisplatin nephrotoxicity (ORadj = 0.24; 95% CI:0.08-0.70; p= 0.009) compared to genotype CC. In contrast, addition of allele A at SLC22A2 rs316019 was associated with increased risk (ORadj = 4.41; 95% CI:1.96-9.88; p < 0.001) while genotype AC was associated with a higher risk of cisplatin nephrotoxicity (ORadj = 5.06; 95% CI:1.69-15.16; p= 0.004) compared to genotype CC. Our study showed that different case definitions led to variability in the genetic risk ascertainment of cisplatin nephrotoxicity. Therefore, consensus on a set of clinically relevant outcome definitions that all such studies should follow is needed.

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.