Nonparametric Analysis of Sequence Heterogeneity Associated with Injection Drug Usage

Mei-Fen Yeh, Jeanne Kowalski, Guang Wen Zhang, Qiajia Shao, Mike Schneider, Alan Templeton, and Richard Markham

Mei-Fen Yeh

East North American Region Meeting of the International Biometric Society, Pittsburgh, PA

March, 2004

An area of importance in HIV genetic research is to examine the association between usage of drugs of abuse and viral sequence diversity within gene regions targeted by antiretroviral therapy. Clonal sequence sampling enables the study of viral diversity within an individual. Since the number of clones is typically, small, often between one and five, and varies in number among subjects, issues arise on how best to incorporate clonal information to examine the relation of interest. In addressing this problem, we propose a simple yet effective approach that estimates genetic diversity by combining information among all pairs of clones to define a composite measure of individual clonal heterogeneity. Based upon this measure, we initially examine the relation of interest by implementing a generalized estimating equation approach. We also discuss an alternative, nonparametric approach that extends our initial measure to examine for example, between-subject heterogeneity and covariate-adjusted heterogeneity. The approach is applied to longitudinal sequence data from the Women’s Interagency HIV Study, where the problem of characterizing the relation between injection drug usage and diversity in the HIV protease region is examined, and the effect of CD4 counts and viral load on this relation.

A History of Injection Drug Use among Women Is Associated with Increased Diversity in the HIV-1 Protease Gene

Mei-Fen Yeh, Jeanne Kowalski, Guang Wen Zhang, Qiajia Shao, Mike Schneider, Alan Templeton, and Richard Markham

Mei-Fen Yeh

11th Conference on Retroviruses and Opportunistic Infections, San Francisco, CA

February, 2004

Introduction: Previous studies from this laboratory have demonstrated a highly significant association between intensity of injection drug use and genetic diversity within the env region of viral clones obtained from the same individual. This finding was consistent with studies indicating that drugs of abuse can enhance viral replication in vitro. The current study is designed to address the hypothesis that injection drug use can enhance genetic diversity within genes targeted by antiretroviral therapy. Such a finding would raise the possibility that injection drug use would result in a higher level of primary resistance to antiretroviral therapy.

Methods:  Study population: The dataset analyzed 124 subject visits by 58 participants in the multicenter Women's Interagency HIV Study (WIHS). Individuals were selected who had never received HAART and who had at least 10,000 copies/ml of viral RNA to prevent re-sampling. Injection drug users were defined as those reporting “yes” to having ever used injection drugs. Sequencing technique: Standard RT-PCR techniques were used to amplify a 1617 base-pair sequence of the HIV-1 pol gene. Analysis of protease gene diversity is included in this report. Heterogeneity measurements: Diversity estimates Genetic diversity is estimated by a heterogeneity measure that combines information among all pairs of clones formed within an individual, by visit. To examine the relationship between genetic diversity, as defined above, and injection drug use, we implemented a generalized estimating equation approach for statistical analysis.

Results: (1) Viral loads were equivalent among Injection drug users (IDU) and non-users, independent of CD4 category. (2) Genetic diversity was significantly greater (p < 0.03) among IDU than among non-users among subjects with CD4 T cell levels > 650. This difference disappeared at lower CD4 T cell levels. (3) In this cross-sectional analysis, a diversity inflection point appeared at CD4 T cell levels of about 600. This is occurring at a much higher level of CD4 count than was observed for envelope diversity in men.

Conclusions: (1) A history of injection drug use is associated with a significantly higher level of genetic diversity at higher CD4 levels. (2) This increase is not associated with evidence of contemporaneous higher counts of viral replication. This finding does not exclude the possibility that previous replication bursts associated with injection drug use gave rise to diversity that persisted after increased levels of replication subsided. (3) The decline in diversity as CD4 T cell levels fall suggest that other selection forces emerge that restrict heterogeneity at lower CD4 counts. (4) The diversity inflection point observed at CD4=600 needs to be confirmed in longitudinal studies of individual subjects and compared to that observed in men.

A Nonparametric Approach to Testing and Characterizing Gene Region Heterogeneity Associated with Phenotype

Jeanne Kowalski

Department of Biostatistics, Johns Hopkins University,  Bloomberg School of Public Health, Baltimore, Maryland

September, 2001

An important focus of genetic research is to study mutation patterns through their impact on phenotype. Of particular interest is the identification of mutations at locations within a gene region that together depict phenotype. This task includes statistical issues of high dimensionality coupled with small sample sizes. For retroviral genomes, such as HIV, these issues are further compounded by the existence of distinct but genetically related viral variants. In this talk I describe two non-parametric approaches, one for comparing and another for characterizing, distributions of a gene region (sequence pair) heterogeneity measure between groups with similar phenotype. For comparing distributions, hypotheses are constructed for testing differential between-group heterogeneity and within-group homogeneity. Group comparisons are made based on either developed asymptotics (extending U-statistic theory to a correlated multivariate two-sample setting) or permutation tests. For characterizing gene region heterogeneity, a method is constructed for identifying potentially important locations and their mutation patterns. The relative importance of locations is evaluated through estimation of their contribution to observed gene region differences; mutation patterns are discerned through location descriptive statistics. As motivation for the methods, I examine the problem of relating differences in a region of the HIV genome to the phenotype, altered viral drug susceptibility.

A Nonparametric Approach to Testing and Translating Gene Region Heterogeneity Associated with Phenotypic Response into Location Heterogeneity

Jeanne Kowalski

Division of Genetics, Boston University, School of Medicine, Boston, Massachusetts

September, 2000

One area of genetic research is to be able to associate gene location differences with some phenotypic response. In particular, interest lies in identifying locations, which together, depict gene region heterogeneity within and between phenotypic groups. This task leads to statistical issues of high dimensionality with typically small sample sizes. In the case of retroviral genomes, such as HIV, the issue is further compounded by the existence of distinct, but genetically related viral variants (quasi-species). In addressing these problems, we propose a non-parametric approach for comparing distributions of a measure of gene region distance between groups with similar phenotype. This gene region distance measure may depend on the number, site and type of mutations, as well as the interactions between them. Group comparisons are made based on either asymptotics or a permutation test, with the relative importance of specific locations evaluated by determining their contribution to observed gene region heterogeneity between phenotypic groups. Additional descriptive statistics are constructed to evaluate differences in mutation patterns between groups, within a location. We motivate the methods by examining the problem of altered HIV drug susceptibility and illustrate their use through characterizing locations attributable to HIV protease gene region differences associated with an (in vitro) phenotypic treatment response.

A Non-Parametric Approach to Relating Genotypes with Phenotypes

Jeanne Kowalski

Massachusetts General Hospital, Biostatistics Center, Harvard School of Medicine, Boston, Massachusetts

April, 2000

Relating HIV-1 Mutation Patterns to Phenotype:  A Nonparametric Approach

Jeanne Kowalski

Joint Statistical Meetings, Baltimore, Maryland

August, 1999

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