#### Mapping genetic variants for nonsense-mediated mRNA decay regulation across human tissues **Background** Nonsense-mediated mRNA decay (NMD) was originally conceived as an mRNA surveillance mechanism to prevent the production of potentially deleterious truncated proteins. Research also shows NMD is an important post-transcriptional gene regulation mechanism selectively targeting many non-aberrant mRNAs. However, how natural genetic variants affect NMD and modulate gene expression remains elusive. **Results** Here we elucidate NMD regulation of individual genes across human tissues through genetical genomics. Genetic variants corresponding to NMD regulation are identified based on GTEx data through unique and robust transcript expression modelling. We identify genetic variants that influence the percentage of NMD-targeted transcripts (pNMD-QTLs), as well as genetic variants regulating the decay efficiency of NMD-targeted transcripts (dNMD- QTLs). Many such variants are missed in traditional expression quantitative trait locus (eQTL) mapping. NMD-QTLs show strong tissue specificity especially in the brain. They are more likely to overlap with disease single-nucleotide polymorphisms (SNPs). Compared to eQTLs, NMD-QTLs are more likely to be located within gene bodies and exons, especially the penultimate exons from the 3’ end. Furthermore, NMD-QTLs are more likely to be found in the binding sites of miRNAs and RNA binding proteins. **Conclusions** We reveal the genome-wide landscape of genetic variants associated with NMD regulation across human tissues. Our analysis results indicate important roles of NMD in the brain. The preferential genomic positions of NMD-QTLs suggest key attributes for NMD regulation. Furthermore, the overlap with disease-associated SNPs and post-transcriptional regulatory elements implicates regulatory roles of NMD-QTLs in disease manifestation and their interactions with other post-transcriptional regulators. ------------------------------ This project provides the pNMD-QTLs and dNMD-QTLs list we discovered in this paper, and also the analysis jupyter notebooks. The `create_phenoBed.ipynb` shows how we grouping transcripts into NMD and non-NMD group, which was the input data for FastQTL software. One should be able to reproduce the NMD-QTLs calling following this. We used this wrapper (https://github.com/francois-a/fastqtl) to launch FastQTL. NOTE: the individual genotype files are confidential and is NOT included in this repo. We obtained the access to that data via GTEx, please contact our P.I. Liang Chen (liang.chen@usc.edu) for more information.