Reconsidering the asymptotic null distribution of likelihood ratio tests for genetic linkage in multivariate variance components models

TL;DR

This paper corrects the previously mischaracterized asymptotic null distribution of likelihood ratio tests in multivariate genetic linkage models, revealing more complex behavior and proposing a faster, more accurate method for significance testing.

Contribution

It provides a theoretical and simulation-based correction to the null distribution, showing it differs from prior chi-square mixture models and introduces a more efficient method for P value computation.

Findings

Previous null distribution estimates were incorrect.

The true distribution has complex features and severe departures from chi-square.

A new faster method for null distribution generation is proposed.

Abstract

Accurate knowledge of the null distribution of hypothesis tests is important for valid application of the tests. In previous papers and software, the asymptotic null distribution of likelihood ratio tests for detecting genetic linkage in multivariate variance components models has been stated to be a mixture of chi-square distributions with binomial mixing probabilities. Here we show, by simulation and by theoretical arguments based on the geometry of the parameter space, that all aspects of the previously stated asymptotic null distribution are incorrect--both the binomial mixing probabilities and the chi-square components. Correcting the null distribution gives more conservative critical values than previously stated, yielding P values that can easily be ten times larger. The true mixing probabilities give the highest probability to the case where all variance parameters are estimated positive, and the mixing components show severe departures from chi-square distributions. Thus, the asymptotic null distribution has complex features that raise challenges for the assessment of significance of multivariate linkage findings. We propose a method to generate an asymptotic null distribution that is much faster than other empirical methods such as gene-dropping, enabling us to obtain P values with higher precision more efficiently.