Stephen R. Williams

Genome-wide meta-analysis of homocysteine and methionine metabolism identifies five one carbon metabolism loci and a novel association of ALDH1L1 with ischemic stroke.

Circulating homocysteine levels (tHcy), a product of the folate one carbon metabolism pathway (FOCM) through the demethylation of methionine, are heritable and are associated with an increased risk of common diseases such as stroke, cardiovascular disease (CVD), cancer and dementia. The FOCM is the sole source of de novo methyl group synthesis, impacting many biological and epigenetic pathways. However, the genetic determinants of elevated tHcy (hyperhomocysteinemia), dysregulation of methionine metabolism and the underlying biological processes remain unclear. We conducted independent genome-wide association studies and a meta-analysis of methionine metabolism, characterized by post-methionine load test tHcy, in 2,710 participants from the Framingham Heart Study (FHS) and 2,100 participants from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, and then examined the association of the identified loci with incident stroke in FHS. Five genes in the FOCM pathway (GNMT [p = 1.60×10−63], CBS [p = 3.15×10−26], CPS1 [p = 9.10×10−13], ALDH1L1 [p = 7.3×10−13] and PSPH [p = 1.17×10−16]) were strongly associated with the difference between pre- and post-methionine load test tHcy levels (ΔPOST). Of these, one variant in the ALDH1L1 locus, rs2364368, was associated with incident ischemic stroke. Promoter analyses reveal genetic and epigenetic differences that may explain a direct effect on GNMT transcription and a downstream affect on methionine metabolism. Additionally, a genetic-score consisting of the five significant loci explains 13% of the variance of ΔPOST in FHS and 6% of the variance in VISP. Association between variants in FOCM genes with ΔPOST suggest novel mechanisms that lead to differences in methionine metabolism, and possibly the epigenome, impacting disease risk. These data emphasize the importance of a concerted effort to understand regulators of one carbon metabolism as potential therapeutic targets.

Comparative efficacy of combination drug therapy in refractory epilepsy

Objective: We retrospectively examined treatment records of developmentally disabled adults with highly refractory epilepsy to determine whether any combinations of 8 of the most commonly used antiepileptic drugs (AEDs) possessed superior efficacy. Methods: We obtained the treatment records from 148 developmentally disabled adults with refractory epilepsy cared for in 2 state-run institutions. These records charted monthly convulsive seizure occurrence and AED regimen over 30 years. We studied the effects of 8 commonly used AEDs alone and in combination on seizure frequency in within-patient comparisons. Results: Out of the 32 most frequently used AED combinations, we found that only the combination of lamotrigine and valproate had superior efficacy, measured against both an aggregate measure of other AED regimens to which patients were exposed, and in head-to-head comparisons with other AED combinations. We also found that while use of 2 concurrent AEDs provided improved efficacy over monotherapy, use of 3 AEDs at a time provided no further benefit over two. Conclusions: These results suggest that at least one AED regimen provides significantly better efficacy in refractory convulsive epilepsy, and that AEDs should be used no more than 2 at a time. Limitations of the study include its retrospective design, lack of randomization, and small sample sizes for some drug combinations. Future prospective trials are needed in this challenging clinical population.

Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial

Background: B vitamins play an important role in homocysteine metabolism, with vitamin deficiencies resulting in increased levels of homocysteine and increased risk for stroke. We performed a genome-wide association study (GWAS) in 2,100 stroke patients from the Vitamin Intervention for Stroke Prevention (VISP) trial, a clinical trial designed to determine whether the daily intake of high-dose folic acid, vitamins B6, and B12 reduce recurrent cerebral infarction. Methods: Extensive quality control (QC) measures resulted in a total of 737,081 SNPs for analysis. Genome-wide association analyses for baseline quantitative measures of folate, Vitamins B12, and B6 were completed using linear regression approaches, implemented in PLINK. Results: Six associations met or exceeded genome-wide significance (P ≤ 5 × 10−08). For baseline Vitamin B12, the strongest association was observed with a non-synonymous SNP (nsSNP) located in the CUBN gene (P = 1.76 × 10−13). Two additional CUBN intronic SNPs demonstrated strong associations with B12 (P = 2.92 × 10−10 and 4.11 × 10−10), while a second nsSNP, located in the TCN1 gene, also reached genome-wide significance (P = 5.14 × 10−11). For baseline measures of Vitamin B6, we identified genome-wide significant associations for SNPs at the ALPL locus (rs1697421; P = 7.06 × 10−10 and rs1780316; P = 2.25 × 10−08). In addition to the six genome-wide significant associations, nine SNPs (two for Vitamin B6, six for Vitamin B12, and one for folate measures) provided suggestive evidence for association (P ≤ 10−07). Conclusion: Our GWAS study has identified six genome-wide significant associations, nine suggestive associations, and successfully replicated 5 of 16 SNPs previously reported to be associated with measures of B vitamins. The six genome-wide significant associations are located in gene regions that have shown previous associations with measures of B vitamins; however, four of the nine suggestive associations represent novel finding and warrant further investigation in additional populations.

Shared genetic susceptibility of vascular-related biomarkers with ischemic and recurrent stroke

Objective: To investigate the genetic contributors to cerebrovascular disease and variation in biomarkers of ischemic stroke. Methods: The Vitamin Intervention for Stroke Prevention Trial (VISP) was a randomized, controlled clinical trial of B vitamin supplementation to prevent recurrent stroke, myocardial infarction, or death. VISP collected baseline measures of C-reactive protein (CRP), fibrinogen, creatinine, prothrombin fragments F1+2, thrombin-antithrombin complex, and thrombomodulin prior to treatment initiation. Genome-wide association scans were conducted for these traits and follow-up replication analyses were performed. Results: We detected an association between CRP single nucleotide polymorphisms (SNPs) and circulating CRP levels (most associated SNP, rs2592902, p = 1.14 × 10−9) in 2,100 VISP participants. We discovered a novel association for CRP level in the AKR1D1 locus (rs2589998, p = 7.3 × 10−8, approaching genome-wide significance) that also is an expression quantitative trait locus for CRP gene expression. We replicated previously identified associations of fibrinogen with SNPs in the FGB and LEPR loci. CRP-associated SNPs and CRP levels were significantly associated with risk of ischemic stroke and recurrent stroke in VISP as well as specific stroke subtypes in METASTROKE. Fibrinogen levels but not fibrinogen-associated SNPs were also found to be associated with recurrent stroke in VISP. Conclusions: Our data identify a genetic contribution to inflammatory and hemostatic biomarkers in a stroke population. Additionally, our results suggest shared genetic contributions to circulating CRP levels measured poststroke and risk for incident and recurrent ischemic stroke. These data broaden our understanding of genetic contributors to biomarker variation and ischemic stroke risk, which should be useful in clinical risk evaluation.

Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN)

Genome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes), and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA). In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of “public controls” and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification) or differential genotype calling (batch effects). The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms.

A Fast Multiple‐Kernel Method With Applications to Detect Gene‐Environment Interaction

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene‐gene or gene‐environment interactions, incorporating variance‐component based methods for population substructure into rare‐variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the “expectation‐maximization (EM)” algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene‐environment interaction, we propose a computationally efficient and statistically rigorous “fastKM” algorithm for multikernel analysis that is based on a low‐rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single‐kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM‐based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene‐by‐vitamin effects on recurrent stroke risk and gene‐by‐age effects on change in homocysteine level.

Association between antiepileptic drug dose and long-term response in patients with refractory epilepsy

Seizures in patients with medically refractory epilepsy remain a substantial clinical challenge, not least because of the dearth of evidence-based guidelines as to which antiepileptic drug (AED) regimens are the most effective, and what doses of these drugs to employ. We sought to determine whether there were regions in the dosage range of commonly used AEDs that were associated with superior efficacy in patients with refractory epilepsy. We retrospectively analyzed treatment records from 164 institutionalized, developmentally disabled patients with refractory epilepsy, averaging 17years of followup per patient. We determined the change in seizure frequency in within-patient comparisons during treatment with the most commonly used combinations of 12 AEDs, and then analyzed the response to treatment by quartile of the dose range for monotherapy with carbamazepine (CBZ), lamotrigine (LTG), valproate (VPA), or phenytoin (PHT), and the combination LTG/VPA. We found that of the 26 most frequently used AED regimens, only LTG/VPA yielded superior efficacy, similar to an earlier study. For the monotherapies, patients who were treated in the lowest quartile of the dose range had significantly better long-term reduction in seizure frequency compared to those treated in the 2nd and 3rd quartiles of the dose range. Patients with paired exposures to CBZ in both the lowest quartile and a higher quartile of dose range experienced an increase in seizure frequency at higher doses, while patients treated with LTG/VPA showed improved response with escalation of LTG dosage. We conclude that in this population of patients with refractory epilepsy, LTG/VPA was the most effective AED combination. The best response to AEDs used in monotherapy was observed at low dosage. This suggests that routine exposure to maximally tolerated AED doses may not be necessary to identify those patients with drug-resistant seizures who will have a beneficial response to therapy. Rather, responders to a given AED regimen may be identified with exposure to low AED doses, with careful evaluation of the response to subsequent titration to identify non-responders or those with exacerbation of seizure frequency at higher doses.

Seeking the “holy grail” of biomarkers to improve stroke risk prediction of clinical scores

Biomarkers are defined as “objective indications of medical state observed from outside the patient—which can be measured accurately and reproducibly.”1 Biomarkers have 3 major potential roles in clinical practice: (1) to help the patient understand their risk of disease, which could lead to direct improvement of quality of life; (2) to direct the patient to make lifestyle changes that could improve health, such as restricting or improving dietary choices, becoming more active, and adhering to a plan laid out in collaboration with their physician; and (3) to direct a medical professional to make a (better) clinical decision based on known risk factors or disease(s) associated with a specific biomarker(s).2 Given the complex pathophysiology of ischemic stroke, biomarkers that can stratify risk and identify those individuals most likely to have a cerebrovascular event are considered the “holy grail” of prognostic tools.