Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft.
10.1016/j.gpb.2018.07.005
- Author:
Shi-Jian ZHANG
1
;
Peiqi MENG
2
;
Jieni ZHANG
2
;
Peizeng JIA
2
;
Jiuxiang LIN
2
;
Xiangfeng WANG
1
;
Feng CHEN
3
;
Xiaoxing WEI
4
Author Information
1. Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China.
2. Department of Orthodontics & Central Laboratory, Peking University School and Hospital of Stomatology, Beijing 100081, China.
3. Department of Orthodontics & Central Laboratory, Peking University School and Hospital of Stomatology, Beijing 100081, China. Electronic address: chenfeng2011@hsc.pku.edu.cn.
4. State Key Laboratory of Plateau Ecology and Agriculture, Medical College of Qinghai University, Xining 810016, China. Electronic address: weixiaoxing@tsinghua.org.cn.
- Publication Type:Journal Article
- Keywords:
Folic acid;
Genetic risk;
Nutritional intervention;
Orofacial cleft;
Vitamin A
- MeSH:
Asian Continental Ancestry Group;
genetics;
China;
ethnology;
Cleft Lip;
genetics;
Cleft Palate;
genetics;
Genome-Wide Association Study;
Humans;
Infant;
Logistic Models;
Machine Learning;
Methylenetetrahydrofolate Reductase (NADPH2);
genetics;
Polymorphism, Single Nucleotide;
Retinol-Binding Proteins, Plasma;
genetics;
Risk Assessment
- From:
Genomics, Proteomics & Bioinformatics
2018;16(5):354-364
- CountryChina
- Language:English
-
Abstract:
The isolated type of orofacial cleft, termed non-syndromic cleft lip with or without cleft palate (NSCL/P), is the second most common birth defect in China, with Asians having the highest incidence in the world. NSCL/P involves multiple genes and complex interactions between genetic and environmental factors, imposing difficulty for the genetic assessment of the unborn fetus carrying multiple NSCL/P-susceptible variants. Although genome-wide association studies (GWAS) have uncovered dozens of single nucleotide polymorphism (SNP) loci in different ethnic populations, the genetic diagnostic effectiveness of these SNPs requires further experimental validation in Chinese populations before a diagnostic panel or a predictive model covering multiple SNPs can be built. In this study, we collected blood samples from control and NSCL/P infants in Han and Uyghur Chinese populations to validate the diagnostic effectiveness of 43 candidate SNPs previously detected using GWAS. We then built predictive models with the validated SNPs using different machine learning algorithms and evaluated their prediction performance. Our results showed that logistic regression had the best performance for risk assessment according to the area under curve. Notably, defective variants in MTHFR and RBP4, two genes involved in folic acid and vitamin A biosynthesis, were found to have high contributions to NSCL/P incidence based on feature importance evaluation with logistic regression. This is consistent with the notion that folic acid and vitamin A are both essential nutritional supplements for pregnant women to reduce the risk of conceiving an NSCL/P baby. Moreover, we observed a lower predictive power in Uyghur than in Han cases, likely due to differences in genetic background between these two ethnic populations. Thus, our study highlights the urgency to generate the HapMap for Uyghur population and perform resequencing-based screening of Uyghur-specific NSCL/P markers.
