High-throughput technologies, such as magnetic resonance imaging (MRI) and DNA/RNA sequencing (DNA-seq/RNA-seq), have been increasingly used in large-scale association studies. Important biological and biomedical findings have been generated using these technologies. The reproducibility of these findings, especially from structural MRI (sMRI) and functional MRI (fMRI) association studies, has recently been heavily debated. There is an urgent demand for a reliable overall reproducibility assessment for large-scale high-throughput association studies. It is also desirable to understand the relationship between study reproducibility and sample size in an experimental design. In this study, we developed a novel measure: the mixture model reproducibility index (MMRI) for assessing study reproducibility of large-scale association studies. We performed study reproducibility analysis for several recent large sMRI/fMRI data sets using MMRI. The advantages of our index were clearly demonstrated, and the sample size requirements for different phenotypes were also clearly established, especially when compared to the widely used Jaccard coefficient (JC). We implemented MMRI to evaluate the reproducibility in two MRI and two RNA sequencing data sets. The reproducibility assessment results were consistent with expectations. Unlike previous studies that showed a large proportion of study findings were not reproducible using existing reproducibility measures, MMRI clearly indicated that such studies are highly reproducible as long as the sample size is large enough. In summary, MMRI is a novel and useful index for assessing study reproducibility, calculating sample sizes and evaluating the similarity between two closely related studies.

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