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Background This network meta-analysis was performed to determine the optimal surgical approach for pancreatoduodenectomy by comparing outcomes after laparoscopic pancreatoduodenectomy, robotic pancreatoduodenectomy and open pancreatoduodenectomy. Methods A systematic search of the PubMed, EMBASE, Scopus, and Web of Science databases was conducted to identify eligible randomized controlled trials and propensity-score matched studies. Results Four randomized controlled trials and 23 propensity-score matched studies comprising a total of 4,945 patients were included for analysis. Operation time for open pancreatoduodenectomy was shorter than both laparoscopic pancreatoduodenectomy (mean difference –57.35, 95% CI 26.25–88.46 minutes) and robotic pancreatoduodenectomy (mean difference –91.08, 95% CI 48.61–133.56 minutes), blood loss for robotic pancreatoduodenectomy was significantly less than both laparoscopic pancreatoduodenectomy (mean difference –112.58, 95% CI 36.95–118.20 mL) and open pancreatoduodenectomy (mean difference –209.87, 95% CI 140.39–279.36 mL), both robotic pancreatoduodenectomy and laparoscopic pancreatoduodenectomy were associated with reduced rates of delayed gastric emptying compared with open pancreatoduodenectomy (odds ratio 0.59, 95% CI 0.39–0.90 and odds ratio 0.69, 95% CI 0.50–0.95, respectively), robotic pancreatoduodenectomy was associated with fewer wound infections compared with open pancreatoduodenectomy (odds ratio 0.35, 95% CI 0.18–0.71), and laparoscopic pancreatoduodenectomy patients enjoyed significantly shorter length of stay compared with open pancreatoduodenectomy (odds ratio 0.43, 95% CI 0.28–0.95). There were no differences in other outcomes. Conclusion This network meta-analysis of high-quality studies suggests that when laparoscopic pancreatoduodenectomy and robotic pancreatoduodenectomy are performed in high-volume centers, short-term perioperative and oncologic outcomes are largely comparable, if not slightly improved, compared with traditional open pancreatoduodenectomy. These findings should be corroborated in further prospective randomized studies.

Recent and rapid human population growth has led to an excess of rare genetic variants that are expected to contribute to an individual’s genetic burden of disease risk. To date, much of the focus has been on rare protein-coding variants, for which potential impact can be estimated from the genetic code, but determining the impact of rare noncoding variants has been more challenging. To improve our understanding of such variants, we combined high-quality genome sequencing and RNA sequencing data from a 17-individual, three-generation family to contrast expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) within this family to eQTLs and sQTLs within a population sample. Using this design, we found that eQTLs and sQTLs with large effects in the family were enriched with rare regulatory and splicing variants (minor allele frequency < 0.01). They were also more likely to influence essential genes and genes involved in complex disease. In addition, we tested the capacity of diverse noncoding annotation to predict the impact of rare noncoding variants. We found that distance to the transcription start site, evolutionary constraint, and epigenetic annotation were considerably more informative for predicting the impact of rare variants than for predicting the impact of common variants. These results highlight that rare noncoding variants are important contributors to individual gene-expression profiles and further demonstrate a significant capability for genomic annotation to predict the impact of rare noncoding variants.