Yaning Yang

Efficiency of single-nucleotide polymorphism haplotype estimation from pooled DNA

The efficiency of single-nucleotide polymorphism haplotype analysis may be increased by DNA pooling, which can dramatically reduce the number of genotyping assays. We develop a method for obtaining maximum likelihood estimates of haplotype frequencies for different pool sizes, assess the accuracy of these estimates, and show that pooling DNA samples is efficient in estimating haplotype frequencies. Although pooling K individuals increases ambiguities, at least for small pool size K and small numbers of loci, the uncertainty of estimation increases <K times that of unpooled DNA. We also develop the asymptotic variance-covariance of maximum likelihood estimates and evaluate the accuracy of variance estimates by Monte Carlo methods. When the sample size of pools is moderately large, the asymptotic variance estimates are rather accurate. Completely or partially missing genotyping information is allowed for in our analysis. Finally, our methods are applied to single-nucleotide polymorphisms in the angiotensinogen gene.

Statistical Methods for Analyzing Microarray Feature Data with Replications

Expression levels in oligonucleotide microarray experiments depend on a potentially large number of factors, for example, treatment conditions, different probes, different arrays, and so on. To dissect the effects of these factors on expression levels, fixed-effects ANOVA methods have previously been proposed. Because we are not necessarily interested in estimating the specific effects of different probes and arrays, we propose to treat these as random effects. Then we only need to estimate their means and variances but not the effect of each of their levels; that is, we can work with a much reduced number of parameters and, consequently, higher precision for estimating expression levels. Thus, we developed a mixed-effects ANOVA model with some random and some fixed effects. It automatically accounts for local normalization between different arrays and for background correction. The method was applied to each of the 6,584 genes investigated in a microarray experiment on two mouse cell lines, PA6/S and PA6/8, where PA6/S enhances proliferation of Pre B cells in vitro but PA6/8 does not. To detect a set of differentially expressed genes (multiple testing problem), we applied the method of controlling the false discovery rate (FDR), which successfully identified 207 genes with significantly different expression levels.

Comparison of two‐phase analyses for case–control genetic association studies

To test for genetic association between a marker and a complex disease using a case-control design, Cochran-Armitage trend tests (CATTs) and Pearsons chi-square test are often employed. Both tests are genotype-based. Song and Elston (Statist. Med. 2006; 25:105-126) introduced the Hardy-Weinberg disequilibrium trend test and combined it with CATT to test for association. Compared to using a single statistic to test for case-control genetic association (referred to as single-phase analysis), two-phase analysis is a new strategy in that it employs two test statistics in one analysis framework, each statistic using all available case-control data. Two such two-phase analysis procedures were studied, in which Hardy-Weinberg equilibrium (HWE) in the population is a key assumption, although the procedures are robust to moderate departure from HWE. Our goal in this article is to study a new two-phase procedure and compare all three two-phase analyses and common single-phase procedures by extensive simulation studies. For illustration, the results are applied to real data from two case-control studies. On the basis of the results, we conclude that with an appropriate choice of significance level for the analysis in phase 1, some two-phase analyses could be more powerful than commonly used test statistics.

Expression of ephrin receptors and ligands in postmortem brains of HIV-infected subjects with and without cognitive impairment.

Despite the successes of combination antiretroviral therapy, HIV-associated neurocognitive disorders persist in many infected individuals. Earlier studies showed that neurocognitive impairment was associated with glutamate toxicity and synaptodendritic damage. We examined alterations in expression of four ephrin genes that are involved in synapse formation and recruitment of glutamate receptors to synapses, in the caudate and anterior cingulate in postmortem brain of cognitively characterized HIV-infected subjects, along with expression of neuronal and astroglial/macroglial markers. Postmortem tissues of HIV-infected and control subjects were obtained from the Manhattan HIV Brain Bank. HIV-infected subjects underwent neurocognitive assessment prior to death. Quantification of mRNA of genes of chemokine receptors and chemokines (CCR5, CXCR4, CCL2), astroglial/microglial markers (GFAP, CD163, CD68), the neuronal marker SNAP25, ephrin receptors EPHA4 and EPHB2, and ephrin ligands EFNB1 and EFNB2 was performed using SYBR Green RT-PCR. Proinflammatory chemokine and glial/macrophage mRNA levels in both regions were significantly greater in HIV+ than in HIV- subjects. Levels of EPHA4 and EFNB2 mRNA in the caudate, and EPHB2 mRNA in anterior cingulate were significantly lower in HIV+ subjects (p < 0.002, p < 0.02, p < 0.05, respectively). These transcripts also showed correlations with immune status and cognitive function within the HIV-infected group. Decreased levels of EFNB2 mRNA in the caudate correlated with lower CD4 counts (P < 0.05). Cognitive associations were limited to the cingulate, where decreased levels of EPHB2 mRNA were associated with better global cognitive status. Decreased cingulate expression of EPHB2 may represent a compensatory mechanism minimizing excitotoxic injury in the face of chronic inflammation.

Robust Genomic Control and Robust Delta Centralization Tests for Case-Control Association Studies

The population-based case-control design is a powerful approach for detecting susceptibility markers of a complex disease. However, this approach may lead to spurious association when there is population substructure: population stratification (PS) or cryptic relatedness (CR). Two simple approaches to correct for the population substructure are genomic control (GC) and delta centralization (DC). GC uses the variance inflation factor to correct for the variance distortion of a test statistic, and the DC centralizes the non-central chi-square distribution of the test statistic. Both GC and DC have been studied for case-control association studies mainly under a specific genetic model (e.g. recessive, additive or dominant), under which an optimal trend test is available. The genetic model is usually unknown for many complex diseases. In this situation, we study the performance of three robust tests based on the GC and DC corrections in the presence of the population substructure. Our results show that, when the genetic model is unknown, the DC- (or GC-) corrected maximum and Pearson’s association test are robust and have good control of Type I error and high power relative to the optimal trend tests in the presence of PS (or CR).

Testing association with interactions by partitioning chi-squares.

Gene‐gene interaction plays an important role in association studies for complex diseases. There have been different approaches to incorporating gene‐gene interactions in candidate gene or genome‐wide association studies, especially for those genes with no marginal effects but with interaction effects. However, there is no general agreement on how interaction should be tested and how main effects and interaction effects act on a significance signal. In this paper, we propose a test of the null hypothesis of no association in terms of interaction effects for two unlinked loci, which is a 4 degrees of freedom (df) chi‐square for two SNPs. The test, derived by contrasting inter‐locus disequilibrium measures between cases and controls, can be viewed as the interaction component of the total Pearson chi‐square. The remaining portion of the total chi‐square can also be used for association analysis, which emphasizes main effects. Simulation studies show that in most situations our interaction test is similar in power to the test based on a logistic regression model but has more power when the genes have no marginal effects. Results also show that single‐locus marginal tests can lose much power if interaction effects dominate main effects. For some specific genetic models, the interaction test may be further partitioned into four 1‐df chi‐squares for individual interaction effect. The interaction pattern can best be demonstrated by the 1‐df chi‐square components. Simulation results show that there is substantial power gain if interaction patterns are properly incorporated in association analysis.

Survival analysis of microarray expression data by transformation models

Many microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Coxs model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816-824] and show that the transformation model provides higher prediction precision than the proportional hazards model.

Testing linkage disequilibrium from pooled DNA: A contingency table perspective

Pooling DNA samples of multiple individuals has been advocated as a method to reduce genotyping costs. Under such a scheme, only the allele counts at each locus, not the haplotype information, are observed. We develop a systematic way for handling such data by formulating the problem in terms of contingency tables, where pooled allele counts are expressed as the margins and the haplotype counts correspond to the unobserved cell counts. We show that the cell frequencies can be uniquely determined from the marginal frequencies under the usual Hardy-Weinberg equilibrium (HWE) assumption and that the maximum likelihood estimates of haplotype frequencies are consistent and asymptotically normal as the number of pools increases. The limiting covariance matrix is shown to be closely related to the extended hypergeometric distribution. Our results are used to derive Wald-type tests for linkage disequilibrium (LD) coefficient using pooled data. It is discovered that pooling is not efficient in testing weak LD despite its efficiency in estimating haplotype frequencies. We also show by simulations that the proposed LD tests are robust to slight deviation from HWE and to minor genotype error. Applications to two real angiotensinogen gene data sets are also provided.

Genome-Wide Conditional Search for Epistatic Disease-Predisposing Variants in Human Association Studies

Genome-wide search for new disease variants, based on well-established variants, has a long history in linkage analysis but is less well-known in genetic case-control association studies. We developed a simple yet highly efficient conditional search method that can find new variants, which are associated with a disease only through epistatic interaction with another variant and do not necessarily have a direct association effect. Our approach is analogous to partitioning of χ2 in a hierarchical design, which is a well-established statistical technique. Applied to previously published data on age-related macular degeneration, our method found two single-nucleotide polymorphisms with genome-wide significant epistatic interaction that could not be found based only on direct main effects.

Full covariate modelling approach in population pharmacokinetics: understanding the underlying hypothesis tests and implications of multiplicity

To clarify the hypothesis tests associated with the full covariate modelling (FCM) approach in population pharmacokinetic analysis, investigate the potential impact of multiplicity in population pharmacokinetic analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity.