Nianjun Liu

Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups

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 multi variable 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.

Detection of Gene × Gene Interactions in Genome-Wide Association Studies of Human Population Data

Empirical evidence supporting the commonality of gene × gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene × gene interactions comprehensively. Currently, there is no single method that is recognized as the ‘best’ for detecting, characterizing, and interpreting gene × gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene × gene interactions in human data.

Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study

Summary Background VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. Methods We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 −1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<5×10−8 in the discovery cohort and p<0·0038 in the replication cohort. Findings The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=1·51×10−8). This association was confirmed in the replication cohort (p=5·04×10−5); analysis of the two cohorts together produced a p value of 4·5×10−12. Individuals heterozygous for the rs12777823 A allele need a dose reduction of 6·92 mg/week and those homozygous 9·34 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement). Interpretation A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population. Funding National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.

VKORC1 polymorphisms, haplotypes and haplotype groups on warfarin dose among African–Americans and European–Americans

BACKGROUND Although the influence of VKORC1 and CYP2C9 polymorphisms on warfarin response has been studied, variability in dose explained by CYP2C9 and VKORC1 is lower among African-Americans compared with European-Americans. This has lead investigators to hypothesize that assessment of VKORC1 haplotypes may help capture a greater proportion of the variability in dose for this under-represented group. However, the inadequate representation of African-Americans and the assessment of a few VKORC1 polymorphisms have hindered this effort. METHODS To determine if VKORC1 haplotypes or haplotype groups explain a higher variability in warfarin dose, we comprehensively assessed VKORC1 polymorphisms in 273 African-Americans and 302 European-Americans. The influence of VKORC1 polymorphisms, race-specific haplotypes and haplotype groups on warfarin dose was evaluated in race-stratified multivariable analyses after accounting for CYP2C9 (*2, *3, *5, *6 and *11) and clinical covariates. RESULTS VKORC1 explained 18% (30% with CYP2C9) variability in warfarin dose among European-Americans and 5% (8% with CYP2C9) among African-Americans. Four common haplotypes in European-Americans and twelve in African-Americans were identified. In each race VKORC1 haplotypes emerged into two groups: low-dose (Group A) and high-dose (Group B). African-Americans had a lower frequency of Group A haplotype (10.6%) compared with European-Americans (35%, p < 0.0001).The variability in dose explained by VKORC1 haplotype or haplotype groups was similar to that of a single informative polymorphism. CONCLUSIONS Our findings support the use of CYP2C9, VKORC1 polymorphisms (rs9934438 or rs9923231) and clinical covariates to predict warfarin dose in both African- and European-Americans. A uniform set of common polymorphisms in CYP2C9 and VKORC1, and limited clinical covariates can be used to improve warfarin dose prediction for a racially diverse population.

Inferring protein–protein interactions through high-throughput interaction data from diverse organisms

MOTIVATION Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.

Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure.

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.

Haplotype-Association Analysis

Association methods based on linkage disequilibrium (LD) offer a promising approach for detecting genetic variations that are responsible for complex human diseases. Although methods based on individual single nucleotide polymorphisms (SNPs) may lead to significant findings, methods based on haplotypes comprising multiple SNPs on the same inherited chromosome may provide additional power for mapping disease genes and also provide insight on factors influencing the dependency among genetic markers. Such insights may provide information essential for understanding human evolution and also for identifying cis-interactions between two or more causal variants. Because obtaining haplotype information directly from experiments can be cost prohibitive in most studies, especially in large scale studies, haplotype analysis presents many unique challenges. In this chapter, we focus on two main issues: haplotype inference and haplotype-association analysis. We first provide a detailed review of methods for haplotype inference using unrelated individuals as well as related individuals from pedigrees. We then cover a number of statistical methods that employ haplotype information in association analysis. In addition, we discuss the advantages and limitations of different methods.

Addition of the α2-Antagonist Yohimbine to Fluoxetine: Effects on Rate of Antidepressant Response

Electrophysiological studies suggest that α2-adrenoceptors profoundly affect monoaminergic neurotransmission by enhancing noradrenergic tone and serotonergic firing rates. Recent reports suggest that α2-antagonism may hasten and improve the response to antidepressant medications. To test this hypothesis, a randomized double-blind controlled trial was undertaken to determine if the combination of an α2-antagonist (yohimbine) with a selective serotonin reuptake agent (SSRI) (fluoxetine) results in more rapid onset of antidepressant action than an SSRI agent alone. In all, 50 subjects with a DSM-IV diagnosis of major depressive disorder confirmed by SCID interview were randomly assigned to receive either fluoxetine 20 mg plus placebo (F/P) or fluxetine 20 mg plus a titrated dose of yohimbine (F/Y). The yohimbine dose was titrated based on blood pressure changes over the treatment period, in a blind-preserving manner. Hamilton depression scale ratings (HDRS) and clinical global impression (CGI) ratings were obtained weekly over a period of 6 weeks. The rate of achieving categorical positive responses was significantly more rapid in the F/Y group compared to the F/P group using both the HDRS and the CGI scales as outcome measures in a survival analysis using a log-rank test (χ2(1)=5.86, p=0.016 and χ2(1)=5.29, p=0.021, respectively). At the last observed visit, 18 (69%) of the 26 F/Y subjects met the response criteria for CGI compared to 10 (42%) of 24 F/P subjects. Using the HDRS criteria, 17 (65%) of 26 F/Y subject vs 10 (42%) of 24 F/P subjects were responders. The addition of the α2-antagonist yohimbine to fluoxetine appears to hasten the antidepressant response. There is also a trend suggesting an increased percentage of responders to the combined treatment at the end of the 6-week trial.

Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model.

Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM). Estimates of individual ancestry can be used in structured association tests (SAT) to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form “semiparametric” method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.

Race influences warfarin dose changes associated with genetic factors

Warfarin dosing algorithms adjust for race, assigning a fixed effect size to each predictor, thereby attenuating the differential effect by race. Attenuation likely occurs in both race groups but may be more pronounced in the less-represented race group. Therefore, we evaluated whether the effect of clinical (age, body surface area [BSA], chronic kidney disease [CKD], and amiodarone use) and genetic factors (CYP2C9*2, *3, *5, *6, *11, rs12777823, VKORC1, and CYP4F2) on warfarin dose differs by race using regression analyses among 1357 patients enrolled in a prospective cohort study and compared predictive ability of race-combined vs race-stratified models. Differential effect of predictors by race was assessed using predictor-race interactions in race-combined analyses. Warfarin dose was influenced by age, BSA, CKD, amiodarone use, and CYP2C9*3 and VKORC1 variants in both races, by CYP2C9*2 and CYP4F2 variants in European Americans, and by rs12777823 in African Americans. CYP2C9*2 was associated with a lower dose only among European Americans (20.6% vs 3.0%, P < .001) and rs12777823 only among African Americans (12.3% vs 2.3%, P = .006). Although VKORC1 was associated with dose decrease in both races, the proportional decrease was higher among European Americans (28.9% vs 19.9%, P = .003) compared with African Americans. Race-stratified analysis improved dose prediction in both race groups compared with race-combined analysis. We demonstrate that the effect of predictors on warfarin dose differs by race, which may explain divergent findings reported by recent warfarin pharmacogenetic trials. We recommend that warfarin dosing algorithms should be stratified by race rather than adjusted for race.