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

Abstract

In positional cloning of disease causing genes, identification of a linked chromosomal region via linkage studies is often followed by fine mapping via association studies. Efficiency can be gained with an intermediate step where confidence regions for the locations of disease genes are constructed. The confidence set inference [CSI; Papachristou and Lin, 2006b] achieves this goal by replacing the traditional null hypothesis of no linkage with a new set of null hypotheses where the chromosomal position under consideration is in tight linkage with a trait locus. This approach was shown to perform favorably compared with several competing methods. Using the duality of confidence sets and hypothesis testing, CSI was proposed for the Mean test statistics with affected sibling pair data (CSI-Mean). We postulate that more efficient confidence sets will result if more efficient test statistics are used in the CSI framework. One promising candidate, the maximum LOD score (MLS) statistic, makes maximum use of available identity by descent information, in addition to handling markers with incomplete polymorphism naturally. We propose a procedure that tests the CSI null hypotheses using the MLS statistic (CSI-MLS). Compared with CSI-Mean, CSI-MLS provides tighter confidence regions over a range of single and two-locus disease models. The MLS test is also shown to be more powerful than the Mean test in testing the CSI null over a wide range of disease models, the advantage being most pronounced for recessive models. In addition, CSI-MLS is computationally much more efficient than CSI-Mean.