Background Identifying genetic disease-susceptible individuals through population screening is considered as a promising approach for disease prevention. DNA mismatch repair (MMR) genes including MLH1, MSH2, MSH6 and PMS2 play essential roles in maintaining microsatellite stability through DNA mismatch repair, and pathogenic variation in MMR genes causes microsatellite instability and is the genetic predisposition for cancer as represented by the Lynch syndrome. While the prevalence and spectrum of MMR variation has been extensively studied in cancer, it remains largely elusive in the general population. Lack of the knowledge prevents effective prevention for MMR variation–caused cancer. In the current study, we addressed the issue by using the Chinese population as a model.
Methods We performed extensive data mining to collect MMR variant data from 18 844 ethnic Chinese individuals and comprehensive analyses for the collected MMR variants to determine its prevalence, spectrum and features of the MMR data in the Chinese population.
Results We identified 17 687 distinct MMR variants. We observed substantial differences of MMR variation between the general Chinese population and Chinese patients with cancer, identified highly Chinese-specific MMR variation through comparing MMR data between Chinese and non-Chinese populations, predicted the enrichment of deleterious variants in the unclassified Chinese-specific MMR variants, determined MMR pathogenic prevalence of 0.18% in the general Chinese population and determined that MMR variation in the general Chinese population is evolutionarily neutral.
Conclusion Our study provides a comprehensive view of MMR variation in the general Chinese population, a resource for biological study of human MMR variation, and a reference for MMR-related cancer applications.
- genetic variation
- DNA repair
- genomic instability
- human genetics
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
Statistics from Altmetric.com
LZ and ZQ contributed equally.
Contributors LZ: method development, analysis, data interpretation, draft manuscript; LZ, ZQ, HT, BT, XWu, XWang, LW, YR, GM, JL, BZ, JSC: data acquisition, data interpretation; SMW: conception, design, analysis, data interpretation, manuscript revision and funding.
Funding This work was funded by Macau Science and Technology Development Fund (085/2017/A2, 0077/2019/AMJ), University of Macau (SRG2017-00097-FHS, MYRG2019-00018-FHS), Faculty of Health Sciences, University of Macau (FHSIG/SW/0007/2020P, Startup fund) (SMW).
Disclaimer This manuscript was not prepared in collaboration with the ‘SG10K_Pilot Study’ and does not necessarily reflect the opinions or views of the ‘SG10K_Pilot Study’.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.