Biomedical subjects

Guohua Zou

Publications and source records attributed to Guohua Zou.

7 recordsLinked to original sources

PubMed2006

Two-stage designs in case-control association analysis.

DNA pooling is a cost-effective approach for collecting information on marker allele frequency in genetic studies. It is often suggested as a screening tool to identify a subset of candidate markers from a very large number of markers to be followed up by more accurate and informative individual genotyping. In this article, we investigate several statistical properties and design issues related to this two-stage design, including the selection of the candidate markers for second-stage analysis, statistical power of this design, and the probability that truly disease-associated markers are ranked among the top after second-stage analysis. We have derived analytical results on the proportion of markers to be selected for second-stage analysis. For example, to detect disease-associated markers with an allele frequency difference of 0.05 between the cases and controls through an initial sample of 1000 cases and 1000 controls, our results suggest that when the measurement errors are small (0.005), approximately 3% of the markers should be selected. For the statistical power to identify disease-associated markers, we find that the measurement errors associated with DNA pooling have little effect on its power. This is in contrast to the one-stage pooling scheme where measurement errors may have large effect on statistical power. As for the probability that the disease-associated markers are ranked among the top in the second stage, we show that there is a high probability that at least one disease-associated marker is ranked among the top when the allele frequency differences between the cases and controls are not <0.05 for reasonably large sample sizes, even though the errors associated with DNA pooling in the first stage are not small. Therefore, the two-stage design with DNA pooling as a screening tool offers an efficient strategy in genomewide association studies, even when the measurement errors associated with DNA pooling are nonnegligible. For any disease model, we find that all the statistical results essentially depend on the population allele frequency and the allele frequency differences between the cases and controls at the disease-associated markers. The general conclusions hold whether the second stage uses an entirely independent sample or includes both the samples used in the first stage and an independent set of samples.

Algorithms↗

PubMed2005

On the sample size requirement in genetic association tests when the proportion of false positives is controlled.

With respect to the multiple-tests problem, recently an increasing amount of attention has been paid to control the false discovery rate (FDR), the positive false discovery rate (pFDR), and the proportion of false positives (PFP). The new approaches are generally believed to be more powerful than the classical Bonferroni one. This article focuses on the PFP approach. It demonstrates via examples in genetic association studies that the Bonferroni procedure can be more powerful than the PFP-control one and also shows the intrinsic connection between controlling the PFP and controlling the overall type I error rate. Since controlling the PFP does not necessarily lead to a desired power level, this article addresses the design issue and recommends the sample sizes that can attain the desired power levels when the PFP is controlled. The results in this article also provide rough guidance for the sample sizes to achieve the desired power levels when the FDR and especially the pFDR are controlled.

Algorithms↗

PubMed2005

Family-based association tests for different family structures using pooled DNA.

DNA pooling is a cost-effective strategy for genomewise association studies to identify disease genes. In the context of family-based association studies, Risch & Teng (1998) mainly considered families of identical structures to detect associations between genetic markers and disease, and suggested possible approaches to incorporating different family types without a thorough study of their properties. However, families collected in real genetic studies often have different structures and, more importantly, the informativeness of each family structure depends on the disease model which is generally unknown. So there is a need to develop and investigate statistical methods to combine information from diverse family types. In this article, we propose a general strategy to incorporate different family types by assigning each family an "optimal" weight in association tests. In addition, we consider measurement errors in our analysis. When we evaluate our approach under different disease models and measurement errors, we find that our weighting scheme may lead to a substantial reduction in sample size required over the approach suggested by Risch & Teng (1998), and measurement errors may have significant impact on the required sample size when the error rates are not negligible.

Computer Simulation↗

PubMed2004

The impacts of errors in individual genotyping and DNA pooling on association studies.

Case-control association studies using unrelated individuals may offer an effective approach for identifying genetic variants that have small to moderate disease risks. In general, two different strategies may be employed to establish associations between genotypes and phenotypes: (1) collecting individual genotypes or (2) quantifying allele frequencies in DNA pools. These two technologies have their respective advantages. Individual genotyping gathers more information, whereas DNA pooling may be more cost effective. Recent technological advances in DNA pooling have generated great interest in using DNA pooling in association studies. In this article, we investigate the impacts of errors in genotyping or measuring allele frequencies on the identification of genetic associations with these two strategies. We find that, with current technologies, compared to individual genotyping, a larger sample is generally required to achieve the same power using DNA pooling. We further consider the use of DNA pooling as a screening tool to identify candidate regions for follow-up studies. We find that the majority of the positive regions identified from DNA pooling results may represent false positives if measurement errors are not appropriately considered in the design of the study.

Algorithms↗

PubMed2004

The estimation of sibling genetic risk parameters revisited.

This report points out that some sibling genetic risk parameters can be regarded as the ratios of the characteristic values in the ascertainment subpopulation. Based on this observation, we reconsider Olson and Cordell's ([2000] Genet. Epidemiol. 18:217-235) and Cordell and Olson's ([2000] Genet. Epidemiol. 18:307-321) estimators, and re-derive these estimators. Furthermore, we provide the closed-form variance estimators. Simulation results suggest that our proposed estimators perform very well, and single ascertainment may be better than complete ascertainment for estimating these genetic parameters.

Alleles↗

PubMed2003

Genotyping error detection through tightly linked markers.

The identification of genotyping errors is an important issue in mapping complex disease genes. Although it is common practice to genotype multiple markers in a candidate region in genetic studies, the potential benefit of jointly analyzing multiple markers to detect genotyping errors has not been investigated. In this article, we discuss genotyping error detections for a set of tightly linked markers in nuclear families, and the objective is to identify families likely to have genotyping errors at one or more markers. We make use of the fact that recombination is a very unlikely event among these markers. We first show that, with family trios, no extra information can be gained by jointly analyzing markers if no phase information is available, and error detection rates are usually low if Mendelian consistency is used as the only standard for checking errors. However, for nuclear families with more than one child, error detection rates can be greatly increased with the consideration of more markers. Error detection rates also increase with the number of children in each family. Because families displaying Mendelian consistency may still have genotyping errors, we calculate the probability that a family displaying Mendelian consistency has correct genotypes. These probabilities can help identify families that, although showing Mendelian consistency, may have genotyping errors. In addition, we examine the benefit of available haplotype frequencies in the general population on genotyping error detections. We show that both error detection rates and the probability that an observed family displaying Mendelian consistency has correct genotypes can be greatly increased when such additional information is available.

Family↗

PubMed2003

Haplotype frequency estimation in the presence of genotyping errors.

Several statistical methods have been proposed to estimate haplotype frequencies, either based on unrelated individuals or based on families. These estimates may yield insights on population genetics as well as associations between candidate regions and disease of interest. One limitation of the existing methods is that all these methods make the implicit assumption that there are no genotyping errors. However, genotyping errors are unavoidable in practice. Numerous methods have been developed to incorporate genotyping errors in genetic studies, but none to date have addressed the issues of haplotype inference in the presence of genotyping errors. In this article, we develop statistical methods for haplotype inference incorporating genotyping errors. We describe how our methods can be applied to analyze unrelated individuals as well as nuclear families. Our simulation results show that the proposed methods perform well in the presence of genotyping errors.

Computer Simulation↗