Association in Multifactorial Traits: How to Deal with Rare Observations?

ABCB1 Polymorphisms and Cold Pressor Pain Responses: Opioid-Dependent Patients on Methadone Maintenance Therapy

BACKGROUND

Methadone is a substrate of the P-glycoprotein efflux transporter, which is encoded by ABCB1 (MDR1), and thus, ABCB1 polymorphisms may influence the transport of methadone at the blood-brain barrier, affecting its adverse effects.

OBJECTIVES

This study investigated the association between ABCB1 polymorphisms and cold pressor pain responses among opioid-dependent patients on methadone maintenance therapy (MMT).

METHODS

Malay male opioid-dependent patients receiving MMT (n = 148) were recruited. Cold pressor pain responses (pain threshold, pain tolerance, and pain intensity) were measured at 0, 2, 4, 8, 12, and 24 hours post-methadone dose. DNA was extracted from whole blood and genotyped for ABCB1 polymorphisms including 1236C>T (rs1128503), 2677G>T/A (rs2032582), and 3435C>T (rs1045642) using the allelic discrimination real-time polymerase chain reaction. Repeated-measure analysis of variance between-group analysis was used to compare the three cold pressor pain responses and ABCB1 polymorphisms (1236C>T, 2677G>T/A, and 3435C>T) according to genotypes and allelic additive models, genotype dominant and recessive models, haplotypes, and diplotypes.

RESULTS

Patients with 2677 GG or 2677G allele had the lowest pain threshold compared with 2677G>T/A genotypes or alleles (p = .007 and .002, respectively). Haplotype analysis showed a significant association between ABCB1 haplotypes and pain threshold (p = .02). Patients with 2677G allele had the lowest pain tolerance compared to those with 2677T and 2677A alleles (2677G < 2677T < 2677A allele carriers; p = .05). In terms of pain intensity scores, patients with 2677 GG or 2677G allele had the highest scores compared to other 2677G>T/A genotypes or alleles (p = .04 and .008, respectively). Haplotype analysis revealed a significant difference between patients with CGC haplotype and those without this haplotype (p = .02).

DISCUSSION

To the best of our knowledge, this study provides the first evidence that ABCB1 polymorphisms are associated with cold pressor pain responses among Malay male patients with opioid dependence on MMT. The results may provide an initial prediction on heightened pain sensitivity or hyperalgesia for individuals who are carriers of the ABCB1 polymorphisms.

Use of diplotypes - matched haplotype pairs from homologous chromosomes - in gene-disease association studies

Alleles, genotypes and haplotypes (combinations of alleles) have been widely used in gene-disease association studies. More recently, association studies using diplotypes (haplotype pairs on homologous chromosomes) have become increasingly common. This article reviews the rationale of the four types of association analyses and discusses the situations in which diplotype-based analyses are more powerful than the other types of association analyses. Haplotype-based association analyses are more powerful than allele-based association analyses, and diplotype-based association analyses are more powerful than genotype-based analyses. In circumstances where there are no interaction effects between markers and where the criteria for Hardy-Weinberg Equilibrium (HWE) are met, the larger sample size and smaller degrees of freedom of allele-based and haplotype-based association analyses make them more powerful than genotype-based and diplotype-based association analyses, respectively. However, under certain circumstances diplotype-based analyses are more powerful than haplotype-based analysis.

On the use of phylogeny-based tests to detect association between quantitative traits and haplotypes

With the increasing availability of genetic data, several SNPs in a candidate gene can be combined into haplotypes to test for association with a quantitative trait. When the number of SNPs increases, the number of haplotypes can become very large and there is a need to group them together. The use of the phylogenetic relationships between haplotypes provides a natural and efficient way of grouping. Moreover, it allows us to identify disease or quantitative trait-related loci. In this article, we describe ALTree-q, a phylogeny-based approach to test for association between quantitative traits and haplotypes and to identify putative quantitative trait nucleotides (QTN). This study focuses on ALTree-q association test which is based on one-way analyses of variance (ANOVA) performed at the different levels of the tree. The statistical properties (type-one error and power rates) were estimated through simulations under different genetic models and were compared to another phylogeny-based test, TreeScan, (Templeton, 2005) and to a haplotypic omnibus test consisting in a one-way ANOVA between all haplotypes. For dominant and additive models ALTree-q is usually the most powerful test whereas TreeScan performs better under a recessive model. However, power depends strongly on the recurrence rate of the QTN, on the QTN allele frequency, and on the linkage disequilibrium between the QTN and other markers. An application of the method on Thrombin Activatable Fibronolysis Inhibitor Antigen levels in European and African samples confirms a possible association with polymorphisms of the CPB2 gene and identifies several QTNs.

Statistical performance of cladistic strategies for haplotype grouping in pharmacogenetics

Haplotypes comprising multiple single nucleotide polymorphisms (SNPs) are popular covariates for capturing the key genetic variation present over a region of interest in the DNA sequence. Although haplotypes can provide a clearer assessment of genetic variation in a region than their component SNPs considered individually, the multi-allelic nature of haplotypes increases the complexity of the statistical models intended to discover association with outcomes of interest. Cladistic methods cluster haplotypes according to the estimates of their genealogical closeness and have been proposed recently as strategies for reducing model complexity and increasing power. Two examples are methods based on a haplotype nesting algorithm described by Templeton et al. (Genetics 1987; 117:343-351) and hierarchical clustering of haplotypes as described by Durrant et al. (Am. J. Hum. Genet. 2004; 75:35-43). In the context of assessing the pharmacogenetic effects of candidate genes, for which high-density SNP data have been gathered, we have conducted a simulation-based case study of the testing and estimation properties of two strategies based on Templeton's algorithm (TA), one being that described by Seltman et al. (Am. J. Hum. Genet. 2001; 68:1250-1263; Genet. Epidemiol. 2003; 25:48-58), as well as the method of Durrant et al. using data from a diabetes clinical trial. Even after adjusting for multiplicity, improvements in power can be realized using cladistic approaches with treatment group sizes in the range expected for standard trials, although these gains may be sensitive to the cladistic structure used. Differences in the relative performance of the cladistic approaches examined were observed with the clustering approach of Durrant et al. showing statistical properties superior to the methods based on TA.

hapConstructor: automatic construction and testing of haplotypes in a Monte Carlo framework

Summary: Haplotypes carry important information that can direct investigators towards underlying susceptibility variants, and hence multiple tagging single nucleotide polymorphisms (tSNPs) are usually studied in candidate gene association studies. However, it is often unknown which SNPs should be included in haplotype analyses, or which tests should be performed for maximum power. We have developed a program, hapConstructor, which automatically builds multi-locus SNP sets to test for association in a case-control framework. The multi-SNP sets considered need not be contiguous; they are built based on significance. An important feature is that the missing data imputation is carried out based on the full data, for maximal information and consistency. HapConstructor is implemented in a Monte Carlo framework and naturally extends to allow for significance testing and false discovery rates that account for the construction process and to related individuals. HapConstructor is a useful tool for exploring multi-locus associations in candidate genes and regions.

Availability: http://www-genepi.med.utah.edu/Genie

Contact:ryan.abo@hsc.utah.edu

Estimating effects of rare haplotypes on failure time using a penalized Cox proportional hazards regression model

Background

This paper describes a likelihood approach to model the relation between failure time and haplotypes in studies with unrelated individuals where haplotype phase is unknown, while dealing with the problem of unstable estimates due to rare haplotypes by considering a penalized log-likelihood.

Results

The Cox model presented here incorporates the uncertainty related to the unknown phase of multiple heterozygous individuals as weights. Estimation is performed with an EM algorithm. In the E-step the weights are estimated, and in the M-step the parameter estimates are estimated by maximizing the expectation of the joint log-likelihood, and the baseline hazard function and haplotype frequencies are calculated. These steps are iterated until the parameter estimates converge. Two penalty functions are considered, namely the ridge penalty and a difference penalty, which is based on the assumption that similar haplotypes show similar effects.

Simulations were conducted to investigate properties of the method, and the association between IL10 haplotypes and risk of target vessel revascularization was investigated in 2653 patients from the GENDER study.

Conclusion

Results from simulations and real data show that the penalized log-likelihood approach produces valid results, indicating that this method is of interest when studying the association between rare haplotypes and failure time in studies of unrelated individuals.

Estimating Haplotype Effects on Dichotomous Outcome for Unphased Genotype Data Using a Weighted Penalized Log-Likelihood Approach

OBJECTIVE

To develop a method to estimate haplotype effects on dichotomous outcomes when phase is unknown, that can also estimate reliable effects of rare haplotypes.

METHODS

In short, the method uses a logistic regression approach, with weights attached to all possible haplotype combinations of an individual. An EM-algorithm was used: in the E-step the weights are estimated, and the M-step consists of maximizing the joint log-likelihood. When rare haplotypes were present, a penalty function was introduced. We compared four different penalties. To investigate statistical properties of our method, we performed a simulation study for different scenarios. The evaluation criteria are the mean bias of the parameter estimates, the root of the mean squared error, the coverage probability, power, Type I error rate and the false discovery rate.

RESULTS

For the unpenalized approach, mean bias was small, coverage probabilities were approximately 95%, power ranged from 15.2 to 44.7% depending on haplotype frequency, and Type I error rate was around 5%. All penalty functions reduced the standard errors of the rare haplotypes, but introduced bias. This trade-off decreased power.

CONCLUSION

The unpenalized weighted log-likelihood approach performs well. A penalty function can help to estimate an effect for rare haplotypes.

Tests of Association Between Quantitative Traits and Haplotypes In A Reduced-Dimensional Space

Candidate gene association tests are currently performed using several intragenic SNPs simultaneously, by testing SNP haplotype or genotype effects in multifactorial diseases or traits. The number of haplotypes drastically increases with an increase in the number of typed SNPs. As a result, large numbers of haplotypes will introduce large degrees of freedom in haplotype-based tests, and thus limit the power of the tests. In this study we propose using the principal component method to reduce the dimension, and then construct association tests on the lower-dimensional space to test the association between haplotypes and a quantitative trait using population-based samples. The proposed method allows ambiguous haplotypes. We use simulation studies to evaluate the type I error rate of the tests, and compare the power of the proposed tests with that of the tests without dimension reduction, and the tests with dimension reduction by merging rare haplotypes. The simulation results show that the proposed tests have correct type I error rates and are more powerful than other tests in most cases considered in our simulation studies.