Progress in research of risk prediction of non-syndromic oral clefts using genetic information.
10.3760/cma.j.cn112338-20220624-00556
- VernacularTitle:非综合征型唇腭裂的遗传预测模型研究进展
- Author:
Si Yue WANG
1
;
He Xiang PENG
1
;
En Ci XUE
1
;
Xi CHEN
1
;
Xue Heng WANG
1
;
Meng FAN
1
;
Meng Ying WANG
1
;
Nan LI
2
;
Jing LI
3
;
Zhi Bo ZHOU
2
;
Hong Ping ZHU
2
;
Yong Hua HU
1
;
Tong WU
4
Author Information
1. Department of Epidemiology and Biostatistics,School of Public Health,Peking University, Beijing 100191, China.
2. Department of Oral and Maxillofacial Surgery, School of Stomatology, Peking University, Beijing 100081, China.
3. Department of Pediatrics, School of Stomatology, Peking University, Beijing 100081, China.
4. Department of Epidemiology and Biostatistics,School of Public Health,Peking University, Beijing 100191, China Institute of Reproductive and Child Health/Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing 100191, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Cleft Palate/genetics*;
Cleft Lip/genetics*;
Genome-Wide Association Study;
Genetic Predisposition to Disease;
Risk Factors;
Polymorphism, Single Nucleotide
- From:
Chinese Journal of Epidemiology
2023;44(3):504-510
- CountryChina
- Language:Chinese
-
Abstract:
Non-syndromic oral cleft (NSOC), a common birth defect, remains to be a critical public health problem in China. In the context of adjustment of childbearing policy for two times in China and the increase of pregnancy at older childbearing age, NSOC risk prediction will provide evidence for high-risk population identification and prenatal counseling. Genome-wide association study and second generation sequencing have identified multiple loci associated with NSOC, facilitating the development of genetic risk prediction of NSOC. Despite the marked progress, risk prediction models of NSOC still faces multiple challenges. This paper summarizes the recent progress in research of NSOC risk prediction models based on the results of extensive literature retrieval to provide some insights for the model development regarding research design, variable selection, model-build strategy and evaluation methods.
