Two-step intermediate fine mapping with likelihood ratio test statistics: applications to Problems 2 and 3 data of GAW15

Abstract

Construction of precise confidence sets of disease gene locations after initial identification of linked regions can improve the efficiency of the ensuing fine mapping effort. We took the confidence set inference, a framework proposed and implemented using the Mean test statistic (CSI-Mean) and improved the efficiency substantially by using a likelihood ratio test statistic (CSI-MLS). The CSI framework requires knowledge of some disease-model-related parameters. In the absence of prior knowledge of these parameters, a two-step procedure may be employed: 1) the parameters are estimated using a coarse map of markers; 2) CSI-Mean or CSI-MLS are applied to construct the confidence sets of the disease gene locations using a finer map of markers, assuming the estimates from Step 1 for the required parameters. In this article we show that the advantages of CSI-MLS over CSI-Mean, previously demonstrated when the required parameters are known, are preserved in this two-step procedure, using both the simulated and real data contributed to Problems 2 and 3 of Genetic Analysis Workshop 15. In addition, our result suggests that microsatellite data, when available, should be used in Step 1. Also explored in detail is the effect of the absence of parental genotypes on the performance of CSI-MLS.

Efficient intermediate fine mapping: confidence set inference with likelihood ratio test statistic

“…They have shown that CSI-Mean maintained the desirable statistical properties with this two-step approach. In a recent application of both CSI-MLS and CSI-Mean to the real and simulated data contributed to GAW15 [Sinha and Luo, 2007], we have found that the advantage of CSI-MLS over CSI-Mean is even more pronounced in a two-step CSI procedure, particularly when parental genotypes are not available and when the sample size is large. The reduction in the length of the CI reaches close to 50% in some cases.…”

“…A comparison of the two procedures in a practical setting where the required parameters are not available has been performed using both the simulated and real data contributed to GAW15 [Sinha and Luo, 2007]. Some results of this comparison will be discussed in the Discussion section.…”

“…In particular, the space of potential disease models over which CSI-MLS outperforms CSI-Mean has been identified. Comparison of CSI-Mean and CSI-MLS in a practical setting where the required disease model parameters are not available has been performed using the GAW15 real and simulated data [Sinha and Luo, 2007].…”

Multiple testing in the genomics era: Findings from Genetic Analysis Workshop 15, Group 15

“…Thus, careful pre-filtering of data is crucial, not only for reducing multiple testing, but also for censoring outliers likely to give false positives. Sinha and Luo [2007] investigated how ''Intermediate Fine Mapping'' techniques could further refine linkage peaks by improving the precision of confidence sets of disease gene locations, termed confidence set inference (CSI). Sinha and Luo [2007], Lam et al [2007], and Huang et al [2007] concluded incorporating biologic/statistical information in the context of SNP selection following genome-wide linkage analyses that were promising strategies for reducing costs associated with genotyping while alleviating multiple testing concerns.…”

“…With respect to data reduction using biologic/statistical information, Huang et al [2007], Lam et al [2007], and Sinha and Luo [2007] evaluated methods for SNP selection following genome-wide linkage analyses. Huang et al [2007] performed expression QTL (eQTL) linkage analysis of expression data to determine how selecting significant SNPs in aggregate for follow-up studies differed from an approach that considered cis-and trans-acting SNPs separately.…”