Dataset Information

Viral Genetic Linkage Analysis in the Presence of Missing Data.

ABSTRACT: Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering can be related to the potential of such individuals to contribute to the spread of the virus, either directly through transmission to their partners or indirectly through further spread of HIV from those partners. Assessment of the extent to which individual (incident or prevalent) viruses are clustered within a community will be biased if only a subset of subjects are observed, especially if that subset is not representative of the entire HIV infected population. To address this concern, we develop a multiple imputation framework in which missing sequences are imputed based on a model for the diversification of viral genomes. The imputation method decreases the bias in clustering that arises from informative missingness. Data from a household survey conducted in a village in Botswana are used to illustrate these methods. We demonstrate that the multiple imputation approach reduces bias in the overall proportion of clustering due to the presence of missing observations.

SUBMITTER: Liu SH

PROVIDER: S-EPMC4547719 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Viral Genetic Linkage Analysis in the Presence of Missing Data.

Liu Shelley H SH Erion Gabriel G Novitsky Vladimir V De Gruttola Victor V

PloS one 20150824 8

Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering ...[more]

PMID: 26301919

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Project description:Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

Project description:BackgroundTo investigate the prevalence of robust conclusions in systematic reviews addressing missing (participant) outcome data via a novel framework of sensitivity analyses and examine the agreement with the current sensitivity analysis standards.MethodsWe performed an empirical study on systematic reviews with two or more interventions. Pairwise meta-analyses (PMA) and network meta-analyses (NMA) were identified from empirical studies on the reporting and handling of missing outcome data in systematic reviews. PMAs with at least three studies and NMAs with at least three interventions on one primary outcome were considered eligible. We applied Bayesian methods to obtain the summary effect estimates whilst modelling missing outcome data under the missing-at-random assumption and different assumptions about the missingness mechanism in the compared interventions. The odds ratio in the logarithmic scale was considered for the binary outcomes and the standardised mean difference for the continuous outcomes. We calculated the proportion of primary analyses with robust and frail conclusions, quantified by our proposed metric, the robustness index (RI), and current sensitivity analysis standards. Cohen's kappa statistic was used to measure the agreement between the conclusions derived by the RI and the current sensitivity analysis standards.ResultsOne hundred eight PMAs and 34 NMAs were considered. When studies with a substantial number of missing outcome data dominated the analyses, the number of frail conclusions increased. The RI indicated that 59% of the analyses failed to demonstrate robustness compared to 39% when the current sensitivity analysis standards were employed. Comparing the RI with the current sensitivity analysis standards revealed that two in five analyses yielded contradictory conclusions concerning the robustness of the primary analysis results.ConclusionsCompared with the current sensitivity analysis standards, the RI offers an explicit definition of similar results and does not unduly rely on statistical significance. Hence, it may safeguard against possible spurious conclusions regarding the robustness of the primary analysis results.

Dataset Information

Viral Genetic Linkage Analysis in the Presence of Missing Data.

Publications

Viral Genetic Linkage Analysis in the Presence of Missing Data.

Similar Datasets

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