A fast and powerful linear mixed model approach for genotype-environment interaction tests in large-scale GWAS.

Genotype-by-environment interaction (GEI or GxE) plays an important role in understanding complex human traits. However, it is usually challenging to detect GEI signals efficiently and accurately while adjusting for population stratification and sample relatedness in large-scale genome-wide association studies (GWAS). Here we propose a fast and powerful linear mixed model-based approach, fastGWA-GE, to test for GEI effect and G + GxE joint effect. Our extensive simulations show that fastGWA-GE outperforms other existing GEI test methods by controlling genomic inflation better, providing larger power and running hundreds to thousands of times faster. We performed a fastGWA-GE analysis of ~7.27 million variants on 452 249 individuals of European ancestry for 13 quantitative traits and five environment variables in the UK Biobank GWAS data and identified 96 significant signals (72 variants across 57 loci) with GEI test P-values < 1 × 10-9, including 27 novel GEI associations, which highlights the effectiveness of fastGWA-GE in GEI signal discovery in large-scale GWAS.

A statistical perspective on baseline adjustment in pharmacogenomic genome-wide association studies of quantitative change.

In pharmacogenetic (PGx) studies, drug response phenotypes are often measured in the form of change in a quantitative trait before and after treatment. There is some debate in recent literature regarding baseline adjustment, or inclusion of pre-treatment or baseline value as a covariate, in PGx genome-wide association studies (GWAS) analysis. Here, we provide a clear statistical perspective on this baseline adjustment issue by running extensive simulations based on nine statistical models to evaluate the influence of baseline adjustment on type I error and power. We then apply these nine models to analyzing the change in low-density lipoprotein cholesterol (LDL-C) levels with ezetimibe + simvastatin combination therapy compared with simvastatin monotherapy therapy in the 5661 participants of the IMPROVE-IT (IMProved Reduction of Outcomes: Vytroin Efficacy International Trial) PGx GWAS, supporting the conclusions drawn from our simulations. Both simulations and GWAS analyses consistently show that baseline-unadjusted models inflate type I error for the variants associated with baseline value if the baseline value is also associated with change from baseline (e.g., when baseline value is a mediator between a variant and change from baseline), while baseline-adjusted models can control type I error in various scenarios. We thus recommend performing baseline-adjusted analyses in PGx GWASs of quantitative change.

Germline HLA landscape does not predict efficacy of pembrolizumab monotherapy across solid tumor types.

We evaluated the impact of class I and class II human leukocyte antigen (HLA) genotypes, heterozygosity, and diversity on the efficacy of pembrolizumab. Seventeen pembrolizumab clinical trials across eight tumor types and one basket trial in patients with advanced solid tumors were included (n > 3,500 analyzed). Germline DNA was genotyped using a custom genotyping array. HLA diversity (measured by heterozygosity and evolutionary divergence) across class I loci was not associated with improved response to pembrolizumab, either within each tumor type evaluated or across all patients. Similarly, HLA heterozygosity at each class I and class II gene was not associated with response to pembrolizumab after accounting for the number of tests conducted. No conclusive association between HLA genotype and response to pembrolizumab was identified in this dataset. Germline HLA genotype or diversity alone is not an important independent determinant of response to pembrolizumab and should not be used for clinical decision-making in patients treated with pembrolizumab.

Current challenges and opportunities for pharmacogenomics: perspective of the Industry Pharmacogenomics Working Group (I-PWG).

Pharmaceutical companies have increasingly utilized genomic data for the selection of drug targets and the development of precision medicine approaches. Most major pharmaceutical companies routinely collect DNA from clinical trial participants and conduct pharmacogenomic (PGx) studies. However, the implementation of PGx studies during clinical development presents a number of challenges. These challenges include adapting to a constantly changing global regulatory environment, challenges in study design and clinical implementation, and the increasing concerns over patient privacy. Advances in the field of genomics are also providing new opportunities for pharmaceutical companies, including the availability of large genomic databases linked to patient health information, the growing use of polygenic risk scores, and the direct sequencing of clinical trial participants. The Industry Pharmacogenomics Working Group (I-PWG) is an association of pharmaceutical companies actively working in the field of pharmacogenomics. This I-PWG perspective will provide an overview of the steps pharmaceutical companies are taking to address each of these challenges, and the approaches being taken to capitalize on emerging scientific opportunities.

A Microdose Cocktail to Evaluate Drug Interactions in Patients with Renal Impairment.

Renal impairment (RI) is known to influence the pharmacokinetics of nonrenally eliminated drugs, although the mechanism and clinical impact is poorly understood. We assessed the impact of RI and single dose oral rifampin (RIF) on the pharmacokinetics of CYP3A, OATP1B, P-gp, and BCRP substrates using a microdose cocktail and OATP1B endogenous biomarkers. RI alone had no impact on midazolam (MDZ), maximum plasma concentration (Cmax ), and area under the curve (AUC), but a progressive increase in AUC with RI severity for dabigatran (DABI), and up to ~2-fold higher AUC for pitavastatin (PTV), rosuvastatin (RSV), and atorvastatin (ATV) for all degrees of RI was observed. RIF did not impact MDZ, had a progressively smaller DABI drug-drug interaction (DDI) with increasing RI severity, a similar 3.1-fold to 4.4-fold increase in PTV and RSV AUC in healthy volunteers and patients with RI, and a diminishing DDI with RI severity from 6.1-fold to 4.7-fold for ATV. Endogenous biomarkers of OATP1B (bilirubin, coproporphyrin I/III, and sulfated bile salts) were generally not impacted by RI, and RIF effects on these biomarkers in RI were comparable or larger than those in healthy volunteers. The lack of a trend with RI severity of PTV and several OATP1B biomarkers, suggests that mechanisms beyond RI directly impacting OATP1B activity could also be considered. The DABI, RSV, and ATV data suggest an impact of RI on intestinal P-gp, and potentially BCRP activity. Therefore, DDI data from healthy volunteers may represent a worst-case scenario for clinically derisking P-gp and BCRP substrates in the setting of RI.

Multi-trait analysis of rare-variant association summary statistics using MTAR.

Integrating association evidence across multiple traits can improve the power of gene discovery and reveal pleiotropy. Most multi-trait analysis methods focus on individual common variants in genome-wide association studies. Here, we introduce multi-trait analysis of rare-variant associations (MTAR), a framework for joint analysis of association summary statistics between multiple rare variants and different traits. MTAR achieves substantial power gain by leveraging the genome-wide genetic correlation measure to inform the degree of gene-level effect heterogeneity across traits. We apply MTAR to rare-variant summary statistics for three lipid traits in the Global Lipids Genetics Consortium. 99 genome-wide significant genes were identified in the single-trait-based tests, and MTAR increases this to 139. Among the 11 novel lipid-associated genes discovered by MTAR, 7 are replicated in an independent UK Biobank GWAS analysis. Our study demonstrates that MTAR is substantially more powerful than single-trait-based tests and highlights the value of MTAR for novel gene discovery.

Genetic Association Reveals Protection against Recurrence of Clostridium difficile Infection with Bezlotoxumab Treatment.

Bezlotoxumab is a human monoclonal antibody against Clostridium difficile toxin B, indicated to prevent recurrence of C. difficile infection (rCDI) in high-risk adults receiving antibacterial treatment for CDI. An exploratory genome-wide association study investigated whether human genetic variation influences bezlotoxumab response. DNA from 704 participants who achieved initial clinical cure in the phase 3 MODIFY I/II trials was genotyped. Single nucleotide polymorphisms (SNPs) and human leukocyte antigen (HLA) imputation were performed using IMPUTE2 and HIBAG, respectively. A joint test of genotype and genotype-by-treatment interaction in a logistic regression model was used to screen genetic variants associated with response to bezlotoxumab. The SNP rs2516513 and the HLA alleles HLA-DRB1*07:01 and HLA-DQA1*02:01, located in the extended major histocompatibility complex on chromosome 6, were associated with the reduction of rCDI in bezlotoxumab-treated participants. Carriage of a minor allele (homozygous or heterozygous) at any of the identified loci was related to a larger difference in the proportion of participants experiencing rCDI versus placebo; the effect was most prominent in the subgroup at high baseline risk for rCDI. Genotypes associated with an improved bezlotoxumab response showed no association with rCDI in the placebo cohort. These data suggest that a host-driven, immunological mechanism may impact bezlotoxumab response. Trial registration numbers are as follows: NCT01241552 (MODIFY I) and NCT01513239 (MODIFY II).IMPORTANCEClostridium difficile infection is associated with significant clinical morbidity and mortality; antibacterial treatments are effective, but recurrence of C. difficile infection is common. In this genome-wide association study, we explored whether host genetic variability affected treatment responses to bezlotoxumab, a human monoclonal antibody that binds C. difficile toxin B and is indicated for the prevention of recurrent C. difficile infection. Using data from the MODIFY I/II phase 3 clinical trials, we identified three genetic variants associated with reduced rates of C. difficile infection recurrence in bezlotoxumab-treated participants. The effects were most pronounced in participants at high risk of C. difficile infection recurrence. All three variants are located in the extended major histocompatibility complex on chromosome 6, suggesting the involvement of a host-driven immunological mechanism in the prevention of C. difficile infection recurrence.

ABCG2 regulatory single-nucleotide polymorphisms alter in vivo enhancer activity and expression.

OBJECTIVES: The expression and activity of the breast cancer resistance protein (ABCG2) contributes toward the pharmacokinetics of endogenous and xenobiotic substrates. The effect of genetic variation on the activity of cis-regulatory elements and nuclear response elements in the ABCG2 locus and their contribution toward ABCG2 expression have not been investigated systematically. In this study, the effect of genetic variation on the in vitro and in vivo enhancer activity of six previously identified liver enhancers in the ABCG2 locus was examined. METHODS: Reference and variant liver enhancers were tested for their ability to alter luciferase activity in vitro in HepG2 and HEK293T cell lines and in vivo using a hydrodynamic tail vein assay. Positive in vivo single-nucleotide polymorphisms (SNPs) were tested for association with gene expression and for altered protein binding in electrophoretic mobility shift assays. RESULTS: Multiple SNPs were found to alter enhancer activity in vitro. Four of these variants (rs9999111, rs12508471, ABCG2RE1*2, and rs149713212) decreased and one (rs2725263) increased enhancer activity in vivo. In addition, rs9999111 and rs12508471 were associated with ABCG2 expression in lymphoblastoid cell lines, lymphocytes, and T cells, and showed increased HepG2 nuclear protein binding. CONCLUSION: This study identifies SNPs within regulatory regions of the ABCG2 locus that alter enhancer activity in vitro and in vivo. Several of these SNPs correlate with tissue-specific ABCG2 expression and alter DNA/protein binding. These SNPs could contribute toward reported tissue-specific variability in ABCG2 expression and may influence the correlation between ABCG2 expression and disease risk or the pharmacokinetics and pharmacodynamics of breast cancer resistance protein substrates.

Lessons from the CAGI-4 Hopkins clinical panel challenge.

The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.

An ensemble approach to accurately detect somatic mutations using SomaticSeq.

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated.

SOX11 identified by target gene evaluation of miRNAs differentially expressed in focal and non-focal brain tissue of therapy-resistant epilepsy patients.

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally control the expression of their target genes via RNA interference. There is increasing evidence that expression of miRNAs is dysregulated in neuronal disorders, including epilepsy, a chronic neurological disorder characterized by spontaneous recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is a common type of focal epilepsy in which disease-induced abnormalities of hippocampal neurogenesis in the subgranular zone as well as gliosis and neuronal cell loss in the cornu ammonis area are reported. We hypothesized that in MTLE altered miRNA-mediated regulation of target genes could be involved in hippocampal cell remodeling. A miRNA screen was performed in hippocampal focal and non-focal brain tissue samples obtained from the temporal neocortex (both n=8) of MTLE patients. Out of 215 detected miRNAs, two were differentially expressed (hsa-miR-34c-5p: mean increase of 5.7 fold (p=0.014), hsa-miR-212-3p: mean decrease of 76.9% (p=0.0014)). After in-silico target gene analysis and filtering, reporter gene assays confirmed RNA interference for hsa-miR-34c-5p with 3'-UTR sequences of GABRA3, GRM7 and GABBR2 and for hsa-miR-212-3p with 3'-UTR sequences of SOX11, MECP2, ADCY1 and ABCG2. Reporter gene assays with mutated 3'-UTR sequences of the transcription factor SOX11 identified two different binding sites for hsa-miR-212-3p and its primary transcript partner hsa-miR-132-3p. Additionally, there was an inverse time-dependent expression of Sox11 and miR-212-3p as well as miR-132-3p in rat neonatal cortical neurons. Transfection of neurons with anti-miRs for miR-212-3p and miR-132-3p suggest that both miRNAs work synergistically to control Sox11 expression. Taken together, these results suggest that differential miRNA expression in neurons could contribute to an altered function of the transcription factor SOX11 and other genes in the setting of epilepsy, resulting not only in impaired neural differentiation, but also in imbalanced neuronal excitability and accelerated drug export.

Genomic architecture of pharmacological efficacy and adverse events.

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

A genome-wide association study identifies novel loci for paclitaxel-induced sensory peripheral neuropathy in CALGB 40101.

PURPOSE: Sensory peripheral neuropathy is a common and sometimes debilitating toxicity associated with paclitaxel therapy. This study aims to identify genetic risk factors for the development of this toxicity. EXPERIMENTAL DESIGN: A prospective pharmacogenetic analysis of patients with primary breast cancer, randomized to the paclitaxel arm of CALGB 40101, was used to identify genetic predictors of the onset and severity of sensory peripheral neuropathy. A genome-wide association study in 855 subjects of European ancestry was conducted and findings were replicated in additional European (n = 154) and African American (n = 117) subjects. RESULTS: A single nucleotide polymorphism in FGD4 was associated with the onset of sensory peripheral neuropathy in the discovery cohort [rs10771973; HR, 1.57; 95% confidence interval (CI), 1.30-1.91; P = 2.6 × 10(-6)] and in a European (HR, 1.72; 95% CI, 1.06-2.80; P = 0.013) and African American (HR, 1.93; 95% CI, 1.13-3.28; P = 6.7 × 10(-3)) replication cohort. There is also evidence that markers in additional genes, including EPHA5 (rs7349683) and FZD3 (rs10771973), were associated with the onset or severity of paclitaxel-induced sensory peripheral neuropathy. CONCLUSIONS: A genome-wide association study has identified novel genetic markers of paclitaxel-induced sensory peripheral neuropathy, including a common polymorphism in FGD4, a congenital peripheral neuropathy gene. These findings suggest that genetic variation may contribute to variation in development of this toxicity. Validation of these findings may allow for the identification of patients at increased risk of peripheral neuropathy and inform the use of an alternative to paclitaxel and/or the clinical management of this toxicity.

Single-molecule polymerase chain reaction reduces bias: application to DNA methylation analysis by bisulfite sequencing.

The treatment of DNA with bisulfite, which converts C to U but leaves 5-methyl-C unchanged, forms the basis of many analytical techniques for DNA methylation analysis. Many techniques exist for measuring the methylation state of a single CpG but, for analysis of an entire region, cloning and sequencing remains the gold standard. However, biases in polymerase chain reaction (PCR) amplification and in cloning can skew the results. We hypothesized that single-molecule PCR (smPCR) amplification would eliminate the PCR amplification bias because competition between templates that amplify at different efficiencies no longer exists. The amplified products can be sequenced directly, thus eliminating cloning bias. We demonstrated this accurate and unbiased approach by analyzing a sample that was expected to contain a 50:50 ratio of methylated to unmethylated molecules: a region of the X-linked FMR1 gene from a human female cell line. We compared traditional cloning and sequencing to smPCR and sequencing. Sequencing smPCR products gave an expected methylated to unmethylated ratio of 48:52, whereas conventional cloning and sequencing gave a biased ratio of 72:28. Our results show that smPCR sequencing can eliminate both PCR and cloning bias and represents an attractive approach to bisulfite sequencing.