Genotype-based risk and pharmacogenetic sampling in clinical trials

A resource for integrated genomic analysis of the human liver

In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.

Genotype-Based Recall Studies in Complex Cardiometabolic Traits

In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.

Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.

Statistical power considerations in genotype-based recall randomized controlled trials

Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

Complement pathway biomarkers and age-related macular degeneration

In the age-related macular degeneration (AMD) 'inflammation model', local inflammation plus complement activation contributes to the pathogenesis and progression of the disease. Multiple genetic associations have now been established correlating the risk of development or progression of AMD. Stratifying patients by their AMD genetic profile may facilitate future AMD therapeutic trials resulting in meaningful clinical trial end points with smaller sample sizes and study duration.

Association of insertion/deletion polymorphism of angiotensin-converting enzyme gene among Malay male hypertensive subjects in response to ACE inhibitors

INTRODUCTION

Several studies show that the insertion/deletion (I/D) polymorphism of the angiotensin-converting enzyme (ACE) gene has been associated with hypertension in various populations. The present study sought to determine the association of the I/D gene polymorphism among Malay male essential hypertensive subjects in response to ACE inhibitors (enalapril and lisinopril).

MATERIALS AND METHODS

A total of 72 patients with newly diagnosed hypertension and 72 healthy subjects were recruited in this study. Blood pressure was recorded from 0 to 24 weeks of treatment with enalapril or lisinopril. Genotyping of the I/D polymorphism was carried out using a standard PCR method.

RESULTS

Statistically significant association of the D allele of the ACE gene was observed between the case and control subjects (p < 0.01). There was a decrease in blood pressure in the patients carrying the DD genotype (SBP=18.5±8.1 mmHg, DBP=15.29±7.1 mmHg) rather than the ID (SBP=4.1±3.3 mmHg, DBP=9.1±3.5 mmHg) and II genotypes (SBP= 3.0±0.2 mmHg, DBP 0.11±6.1 mmHg) of the ACE gene.

CONCLUSION

Patients carrying the DD genotype had higher blood pressure-lowering response when treated with ACE inhibitors enalapril or lisinopril than those carrying ID and II genotypes, suggesting that the D allele may be a possible genetic marker for essential hypertension among Malay male subjects.

Multilocus genetic profiling to empower drug trials and predict brain atrophy

Designers of clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10–20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials.

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ApoE genotype status helps enrich MCI trials, using a structural MRI outcome measure.

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CLU, PICALM and CR1 risk genes boost potential MCI trial power beyond ApoE alone.

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CLU, PICALM and CR1 show significant, aggregate effects on TBM maps of brain atrophy.

Pharmacogenetic Study on the Effect of Rivastigmine on PS2 and APOE Genes in Iranian Alzheimer Patients

Background/Aims

Alzheimer disease (AD) is a complex and genetically heterogeneous disorder, and certain genes such as PS2 and APOE4 contribute to the development of AD. Due to its heterogeneity, AD-predisposing genes could vary in different populations. Moreover, not all AD patients will respond to the same therapy. We specifically investigated the effect ofrivastigmine (Exelon) on PS2 and APOE genes in Iranian AD patients.

Methods

A total of 100 AD patients, 67 patients with sporadic AD (SAD) and 33 patients with familial AD (FAD), receiving rivastigmine therapy and 100 healthy controls were studied. PCR-RFLP was used for genotyping of PS2 and APOE.

Results

We found a positive association between the PS2 –A allele and SAD patients (p^c = 0.01), and the PS2 +A/–A genotype was significantly more frequent in SAD than FAD patients (p^c = 0.009). The APOE4 allele was associated with total AD, SAD and FAD (p^c = 0.000002). Patients with the PS2 +A/–A genotype and bigenic genotypes of +A/–A·∊3/∊3 and +A/–A·∊3/∊4 were the best responders to Exelon therapy, and those with the PS2 +A/+A and APOE ∊3/∊4 genotypes were the worst responders.

Conclusion

Our findings suggest that the PS2 and APOE4 alleles and genotypes affect both AD risk and response to rivastigmine therapy.

Type 2 diabetes and obesity: genomics and the clinic

Type 2 diabetes (T2D) and obesity represent major challenges for global public health. They are at the forefront of international efforts to identify the genetic variation contributing to complex disease susceptibility, and recent years have seen considerable success in identifying common risk-variants. Given the clinical impact of molecular diagnostics in rarer monogenic forms of these diseases, expectations have been high that genetic discoveries will transform the prospects for risk stratification, development of novel therapeutics and personalised medicine. However, so far, clinical translation has been limited. Difficulties in defining the alleles and transcripts mediating association effects have frustrated efforts to gain early biological insights, whilst the fact that variants identified account for only a modest proportion of observed familiarity has limited their value in guiding treatment of individual patients. Ongoing efforts to track causal variants through fine-mapping and to illuminate the biological mechanisms through which they act, as well as sequence-based discovery of lower-frequency alleles (of potentially larger effect), should provide welcome acceleration in the capacity for clinical translation. This review will summarise recent advances in identifying risk alleles for T2D and obesity, and existing contributions to understanding disease pathology. It will consider the progress made in translating genetic knowledge into clinical utility, the challenges remaining, and the realistic potential for further progress.

Neuroendocrine tumors in children and young adults: rare or not so rare

This review focuses on neuroendocrine tumors (NETs) that arise in the diffuse neuroendocrine system; these rare tumors can develop in any organ that has dispersed single endocrine cells, for example, the intestine, or in an organ that has clusters of endocrine cells, for example, pancreatic islets. Previously considered benign, NETs are now recognized to recur locally or metastasize to liver and bone if not completely excised early in their course of development. This article summarizes the epidemiology and reviews the diagnostic and therapeutic challenges of NETs in children and youth, noting especially those NETs that are more prevalent in young people than in older adults.

Longitudinal study of a 9p21.3 SNP using a national electronic healthcare database

AIMS

Genome-wide association studies have identified a number of SNPs associated with complex disease. The longitudinal significance of these variants is uncertain and clinical genomic studies are required to elucidate what clinical value these variants have. Linking DNA to clinical health information databases is a powerful and potentially low-cost means of performing such research. Here, we describe a proof-of-principle study demonstrating the potential of this method.

MATERIALS & METHODS

A total of 376 individuals presenting to a hospital with severe coronary artery disease were enrolled into a prospective cohort study. DNA, demographic data, ethnicity and other clinical information was collected in an electronic database. Genotyping for SNPs rs2383207 and rs10757278 was performed using Sequenom^® (CA, USA) matrix-assisted laser desorption/ionisation-time of flight mass spectrometry. Health outcomes were tracked from when patients were discharged from the hospital using the New Zealand Health Information Service (Wellington, New Zealand).

RESULTS

A total of 253 (67%) patients were of New Zealand European descent, 47 (13%) patients were of Maori descent and 21 (6%) were of Pacific Island ancestry. The Maori and Pacific Island group were younger at presentation (63 ± 11 vs 70 ± 9 years of age; p < 0.0001) and had a higher prevalence of cardiovascular risk factors. The frequency of the at-risk rs2383207 G allele in the Maori and Pacific group was 70%, compared with 54% in Europeans (p = 0.002). Similarly, the rs10757278 G allele was also present at a higher frequency (68 vs 52%; p = 0.003). No association was seen between the rs10757278 SNP and cardiovascular risk factors or markers of disease severity. GA and GG individuals had a higher rate of cardiovascular (p = 0.04) and all-cause death (p = 0.02).

CONCLUSION

The linking of genetic data to electronic medical databases is an effective tool to assess the longitudinal effect of gene variants on health outcomes and will aid in the implementation of personalized medicine. Larger sample sizes with longer study duration may yield clinically useful information that aids preventative healthcare.