There have been increasing efforts to relate drug efficacy and disease predisposition with genetic polymorphisms. We present statistical tests for association of haplotype frequencies with discrete and continuous traits in samples of unrelated individuals. Haplotype frequencies are estimated through the expectation-maximization algorithm, and each individual in the sample is expanded into all possible haplotype configurations with corresponding probabilities, conditional on their genotype. A regression-based approach is then used to relate inferred haplotype probabilities to the response. The relationship of this technique to commonly used approaches developed for case-control data is discussed. We confirm the proper size of the test under H₀ and find an increase in power under the alternative by comparing test results using inferred haplotypes with single-marker tests using simulated data. More importantly, analysis of real data comprised of a dense map of single nucleotide polymorphisms spaced along a 12-cM chromosomal region allows us to confirm the utility of the haplotype approach as well as the validity and usefulness of the proposed statistical technique. The method appears to be successful in relating data from multiple, correlated markers to response.
Little is known for certain about the genetics of schizophrenia. The advent of genomewide association has been widely anticipated as a promising means to identify reproducible DNA sequence variation associated with this important and debilitating disorder. A total of 738 cases with DSM-IV schizophrenia (all participants in the CATIE study) and 733 group-matched controls were genotyped for 492 900 single-nucleotide polymorphisms (SNPs) using the Affymetrix 500K two-chip genotyping platform plus a custom 164K fill-in chip. Following multiple quality control steps for both subjects and SNPs, logistic regression analyses were used to assess the evidence for association of all SNPs with schizophrenia. We identified a number of promising SNPs for follow-up studies, although no SNP or multimarker combination of SNPs achieved genomewide statistical significance. Although a few signals coincided with genomic regions previously implicated in schizophrenia, chance could not be excluded. These data do not provide evidence for the involvement of any genomic region with schizophrenia detectable with moderate sample size. However, a planned genomewide association study for response phenotypes and inclusion of individual phenotype and genotype data from this study in meta-analyses hold promise for eventual identification of susceptibility and protective variants.
Warfarin-dosing algorithms incorporating CYP2C9 and VKORC1 ؊1639G>A improve dose prediction compared with algorithms based solely on clinical and demographic factors. However, these algorithms better capture dose variability among whites than Asians or blacks. Herein, we evaluate whether other VKORC1 polymorphisms and haplotypes explain additional variation in warfarin dose beyond that explained by VKORC1 ؊1639G>A among Asians (n ؍ 1103), blacks (n ؍ 670), and whites (n ؍ 3113).Participants were recruited from 11 countries as part of the International Warfarin Pharmacogenetics Consortium effort. Evaluation of the effects of individual VKORC1 single nucleotide polymorphisms (SNPs) and haplotypes on warfarin dose used both univariate and multivariable linear regression. VKORC1 ؊1639G>A and 1173C>T individually explained the greatest variance in dose in all 3 racial groups. Incorporation of additional VKORC1 SNPs or haplotypes did not further improve dose prediction.VKORC1 explained greater variability in dose among whites than blacks and Asians. Differences in the percentage of variance in dose explained by VKORC1 across race were largely accounted for by the frequency of the ؊1639A (or 1173T) allele. Thus, clinicians should recognize that, although at a population level, the contribution of VKORC1 toward dose requirements is higher in whites than in nonwhites; genotype predicts similar dose requirements across racial groups. (Blood. 2010;115(18):3827-3834) IntroductionWarfarin, the most commonly prescribed anticoagulant, exhibits large interpatient variability in dose requirements. Patient-specific factors (eg, age, body size, race, concurrent diseases, and medications) explain some of the variability in warfarin dose, but genetic factors influencing warfarin response explain a significantly higher proportion of the variability in dose. 1 Candidate-gene association studies 2-22 have identified 2 genes responsible for the main proportion of the genetic effect: CYP2C9, which codes for the enzyme cytochrome P450 2C9 that metabolizes S-warfarin, 23,24 and VKORC1, which codes for warfarin's target, vitamin K epoxide reductase. 25,26 The influence of CYP2C9 and VKORC1 has also been confirmed by genome-wide association studies among whites. 27,28 These studies suggest that identification of common variants in other genes exhibiting influence of magnitude similar to that of CYP2C9 and VKORC1 is unlikely in whites. The most influential CYP2C9 polymorphisms are nonsynonymous coding variants resulting in reduced enzyme activity and decreased metabolic capacity. [29][30][31] In contrast, common VKORC1 variants associated with warfarin dose are noncoding polymorphisms, the effects of which are thought to be mediated through differential expression of the VKOR protein. 32 These polymorphisms are within a region of strong linkage disequilibrium (LD) among patients of European ancestry; thus, they may all point to the same common causal polymorphism. 10,14 However, neither the causative VKORC1 polymorphism nor the molecula...
Solitary fibrous tumors are an uncommon sarcoma type characterized by NAB2-STAT6 gene fusion. While solitary fibrous tumors metastasize in 5-25% of cases, it has historically been challenging to determine which specific tumor and patient characteristics predict aggressive behavior. We previously reported on a novel risk stratification scheme for solitary fibrous tumors incorporating patient age, tumor size, and mitotic activity to predict risk of metastasis. Herein we validate this risk stratification scheme in an independent, lower-risk population of 79 patients with primary non-meningeal solitary fibrous tumors, and propose incorporating tumor necrosis as a fourth variable to further improve the risk score. Fifty-seven percent of cases were considered low risk, 29% intermediate risk, and 14% high risk for metastasis. Of 50 patients with sufficient clinical follow-up data, no metastases developed in the low-risk patients (n=23), while there was a 7% 10-year metastatic risk in the intermediate risk group (n=17), and a 49% 5-year metastatic risk for the high-risk patients (n=10). When tumor necrosis was added as a fourth variable to the model, predictive power was enhanced. Under the revised stratification, the proportion of tumors identified as low risk increased to 66%, with no metastasis at 10 years, intermediate risk cases comprised 24% with 10% risk of metastasis at 10 years, and high risk comprised 10% of cases with 73% risk of metastasis at 5 years. In Kaplan-Meier analysis, this fourth-variable stratification provided significant discrimination between the risk groups (P=0.0005). These findings confirmed the clinical utility of our previously published risk stratification model and support the inclusion of necrosis as a fourth variable in the model.
Hereditary multiple exostoses is an autosomal dominant disorder that is characterized by short stature and multiple, benign bone tumours. In a majority of families, the genetic defect (EXT1) is linked to the Langer-Giedion syndrome chromosomal region in 8q24.1. From this region we have cloned and characterized a cDNA which spans chromosomal breakpoints previously identified in two multiple exostoses patients. Furthermore, the gene harbours frameshift mutations in affected members of two EXT1 families. The cDNA has a coding region of 2,238 bp with no apparent homology to other known gene sequences and thus its function remains elusive. However, recent studies in sporadic and exostosis-derived chondrosarcomas suggest that the 8q24.1-encoded EXT1 gene may have tumour suppressor function.
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