Objective, noncontact measurement of central corneal thickness with the Pentacam Scheimpflug system and OLCR pachymeter was convenient and yielded excellent intraoperator repeatability and interoperator reproducibility. Central corneal thickness values obtained with the Pentacam were similar to those obtained with both the OLCR pachymeter and an US pachymeter. Further research is needed to corroborate whether central corneal thickness measurements by the Pentacam and OLCR devices can be used interchangeably and are more clinically useful than US pachymetry.

CYP2C9 and VKORC1 genetic variants are associated with low and intermediate warfarin dose requirements, but markers of high doses are less well characterized. We analyzed the VKORC1 coding sequence and known CYP2C9 and VKORC1 polymorphisms in 15 selected warfarin-resistant (dose, 80 to 185 mg/wk) and 8 warfarinsensitive patients (7 to 13 mg/wk) and 99 unselected controls (8 to 105 mg/wk). We identified a coding VKORC1 Asp36Tyr polymorphism in 7 of 15 resistant compared with 0 of 8 sensitive patients (P ‫؍‬ .026) Carriers of Asp36Tyr in the control group (8 of 99) required significantly higher warfarin doses of 80.9 ؎ 10.1 mg/wk compared with 42.7 ؎ 7.5 mg/wk in noncarriers (F ‫؍‬ 9.79, P ‫؍‬ .002). Asp36Tyr was significantly associated with doses of more than 70 mg/wk (odds ratio, 13.0; 95% confidence limit, 1.3 to 124.2), while doses of 20 to 70 mg/wk were associated with Asp36Tyr (partial r 2 ‫؍‬ .11; P ‫؍‬ .004), CYP2C9*2 and *3 (r 2 ‫؍‬ .08; P ‫؍‬ .01), and VKORC1*2 and *3 markers (r 2 ‫؍‬ .05; P ‫؍‬ .05). All Asp36Tyr carriers also had VKORC1*1 tag-single nucleotide polymorphisms (tag-SNPs) indicating a new haplotype. Asp36Tyr was common in Jewish ethnic groups of Ethiopian (15%) and Ashkenazi (4%) origin. We suggest that Asp36Tyr is a new marker of the high end of the warfarin dosing range. IntroductionReduced warfarin dose requirements are dictated by the CYP2C9*2 and *3 genetic variants due to decreased S-warfarin metabolic clearance and by the VKORC1*2 haplotype (or H1 and H2) with decreased VKORC1 activity and lower ambient reduced vitamin K levels. [1][2][3][4][5][6][7] Variants affecting the intermediate range of warfarin doses include the VKORC1*3 and *4 (or H7, H8, and H9) haplotypes. [4][5] At the other end of the dose spectrum, several rare VKORC1 mutations have been described, including the original report on 4 distinct mutations in warfarin-resistant individuals, 8 a rare Val66Met mutation associated with high warfarin doses, 9-10 and an Asp36Tyr mutation described in 2 patients with doses of 40 to 50 mg/wk 11 However, genetic effects on the highest warfarin doses (more than 80 mg/wk), which are met in clinical practice, have been less well characterized. In an attempt to identify novel markers of warfarin resistance, we performed sequence analysis of all the coding exons in the VKORC1 gene in a selected group of warfarin-resistant and warfarin-sensitive patients, including an analysis of known polymorphisms in CYP2C9 and VKORC1. We validated our findings in a series of unselected warfarin-treated patients described in our previous studies. [12][13] Patients, materials, and methods Patients were at stable anticoagulation (therapeutic international normalized ratio [INR] in 4 clinic visits). Resistant patients were defined by warfarin dose requirements of at least 80 mg/wk in the absence of known dose-increasing factors and sensitive patients by doses of 13 mg/wk or less.The controls were 99 previously described patients recruited as an unselected consecutive series. We recorded patient sex, a...

For DNA-based tests that assess genetic predisposition to coronary heart disease (CHD) to be of clinical value, they need to provide information over and above conventional risk factors (CRFs) currently used in risk algorithms, such as the Framingham Risk Score, 1 which incorporates CRFs such as age, gender, blood lipid concentrations, blood pressure, body mass index, family history, and smoking habit. To achieve this, several hurdles must be passed.The first challenge is to identify a set of common singlenucleotide polymorphisms (SNPs) at loci associated with CHD risk. Over the last 10 to 15 years, this has been done by use of a "candidate gene" approach through association studies in prospective analysis or case-control studies, ie, comparing SNP genotype or allele frequency between groups of individuals with CHD and healthy subjects. Several of the genes, chosen because of their key role in processes that predispose to atherosclerosis, have meta-analysis-confirmed effects on risk of CHD, 2 the best example of which is the APOE gene, which encodes apolipoprotein E, with 3 common isoforms that are associated with strong effects on plasma lipids and more modest effects on risk of CHD. 3 This "hypothesis-driven" search for useful genetic variants provides the foundation for the development of genetic CHD risk profiles, and in the last 2 years, it has been enhanced by technical advances that have allowed "hypothesis-free" genome-wide association studies (GWASs), primarily in a case-control setting. Although the list of identified CHD-risk loci and SNPs will clearly grow, we have at least the basis to start the examination of their potential clinical utility.The second set of challenges is to obtain a robust estimate of the size of the risk effects associated with these SNPs. This requires population-based prospective studies to avoid bias, because estimates in the case-control setting, although efficient for gene discovery, are a suboptimal design for the evaluation of predictive performance of a marker. In addition, information is needed on the risk-allele prevalence between countries and by race and ethnicity, as well as any differences in risk-effect size in these different groups and whether the effect is modified by gender or by the presence of other genes or environmental factors (ie, the context dependence of the effect). To achieve this, genotyping of large data sets will be required, and robust estimates will require that data be combined from several different studies.Third, the clinical utility of adding these genetic risk scores to the CRF algorithms must be examined, in most cases by use of a simplistic additive model, and the most appropriate clinical setting for its application must be explored given concerns about the psychological impact of DNA testing and confidentiality issues. The final set of challenges, given that the SNPs in the currently identified loci do not represent the full heritability estimate for CHD risk, involves determining how newly emerging data from post-GWAS research can be i...