KDRD: assessing discovery replicability based on summary association statistics from a single study
Replicability is essential for reliable discoveries in large-scale association studies. However, conventional replicability assessments typically require independent datasets or repeated data splitting, which may be constrained by data availability, computational burden and reduced sample sizes. Here, we developed Kernel Density-based Replicability of Discovery (KDRD), a statistical framework for…
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