Sample size requirement for detecting interference under the chi-square model.

Abstract

The chi-square model (CHS) of recombination has been studied extensively, in recent years, for its ability of capturing the process and estimating the level of crossover interference. Exploration thus far shows that this model yields much better fits to human genetic data than Haldane's no-interference model, and the explicit level of interference can be easily estimated as well. This paper provides calculations of sample sizes required to detect interference under CHS for a variety of settings. Two data types, fully informative meioses and phase-unknown backcross families, are studied. Under each setting, we calculate the number of meioses/families needed to ensure that the expected log-likelihood difference between the chi(2) interference model and Haldane's no-interference model exceeds a prespecified threshold. It is found that joint consideration of multiple (more than three) markers dramatically reduces the number of meioses/families needed when compared to an analysis based on three-point data, a traditional setting for detecting interference using three-point tests. The results indicate that the numbers of meioses needed to detect interference under CHS are well within the reach of most genetic mapping studies.