{"title":"基于遗传变异组合的黑色素瘤发病率预测","authors":"G. Ntritsos, E. Evangelou","doi":"10.1109/SEEDA-CECNSM53056.2021.9566247","DOIUrl":null,"url":null,"abstract":"The occurrence of melanoma is a composite process that implicates the interaction of phenotypic, environmental, and genetic risk factors. We constructed genetic risk models, with the aim to assess their predictive performance on melanoma risk. Summary level data from the largest meta-analysis of genome-wide association studies for melanoma, up to date, were used for the construction of weighted genetic risk scores. We used six different p-value thresholds for genetic variants inclusion. We evaluated our genetic risk scores in 2,862 events of incident melanoma and 321,789 cancer-free controls from the UK Biobank, a prospective cohort study of 500,000 participants. Using AUCs, we compared the predictive ability of the different genetic risk scores. Genetic risk scores were strongly associated melanoma risk. Odds Ratios ranged from 1.478 to 1.528. The predictive ability of the genetic risk scores ranged from 0.6234 to 0.6328 showing a moderate performance. Our study suggests that when the p-value threshold for genetic variants inclusion become more tolerant, the prediction performance of the model improved. Validation of the results in larger populations, as well as Southern European populations is needed.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of melanoma incidence based on combination of genetic variants\",\"authors\":\"G. Ntritsos, E. Evangelou\",\"doi\":\"10.1109/SEEDA-CECNSM53056.2021.9566247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The occurrence of melanoma is a composite process that implicates the interaction of phenotypic, environmental, and genetic risk factors. We constructed genetic risk models, with the aim to assess their predictive performance on melanoma risk. Summary level data from the largest meta-analysis of genome-wide association studies for melanoma, up to date, were used for the construction of weighted genetic risk scores. We used six different p-value thresholds for genetic variants inclusion. We evaluated our genetic risk scores in 2,862 events of incident melanoma and 321,789 cancer-free controls from the UK Biobank, a prospective cohort study of 500,000 participants. Using AUCs, we compared the predictive ability of the different genetic risk scores. Genetic risk scores were strongly associated melanoma risk. Odds Ratios ranged from 1.478 to 1.528. The predictive ability of the genetic risk scores ranged from 0.6234 to 0.6328 showing a moderate performance. Our study suggests that when the p-value threshold for genetic variants inclusion become more tolerant, the prediction performance of the model improved. Validation of the results in larger populations, as well as Southern European populations is needed.\",\"PeriodicalId\":68279,\"journal\":{\"name\":\"计算机工程与设计\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"计算机工程与设计\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/SEEDA-CECNSM53056.2021.9566247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"计算机工程与设计","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/SEEDA-CECNSM53056.2021.9566247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of melanoma incidence based on combination of genetic variants
The occurrence of melanoma is a composite process that implicates the interaction of phenotypic, environmental, and genetic risk factors. We constructed genetic risk models, with the aim to assess their predictive performance on melanoma risk. Summary level data from the largest meta-analysis of genome-wide association studies for melanoma, up to date, were used for the construction of weighted genetic risk scores. We used six different p-value thresholds for genetic variants inclusion. We evaluated our genetic risk scores in 2,862 events of incident melanoma and 321,789 cancer-free controls from the UK Biobank, a prospective cohort study of 500,000 participants. Using AUCs, we compared the predictive ability of the different genetic risk scores. Genetic risk scores were strongly associated melanoma risk. Odds Ratios ranged from 1.478 to 1.528. The predictive ability of the genetic risk scores ranged from 0.6234 to 0.6328 showing a moderate performance. Our study suggests that when the p-value threshold for genetic variants inclusion become more tolerant, the prediction performance of the model improved. Validation of the results in larger populations, as well as Southern European populations is needed.
期刊介绍:
Computer Engineering and Design is supervised by China Aerospace Science and Industry Corporation and sponsored by the 706th Institute of the Second Academy of China Aerospace Science and Industry Corporation. It was founded in 1980. The purpose of the journal is to disseminate new technologies and promote academic exchanges. Since its inception, it has adhered to the principle of combining depth and breadth, theory and application, and focused on reporting cutting-edge and hot computer technologies. The journal accepts academic papers with innovative and independent academic insights, including papers on fund projects, award-winning research papers, outstanding papers at academic conferences, doctoral and master's theses, etc.