Identification of eight genomic protective alleles for mitochondrial diabetes by Kinship‐graph convolutional network

Abstract

ABSTRACT Aims Nearly 85% of maternally inherited diabetes and deafness (MIDD) are caused by the m.3243A>G mutation in the mitochondrial DNA. However, the clinical phenotypes of MIDD may also be influenced by the nuclear genome, this study aimed to investigate nuclear genome variants that influence clinical phenotypes associated with m.3243A>G mutation in MIDD based on whole‐genome sequencing of the patients belonging to pedigrees. Materials and Methods We analyzed a whole‐genome sequencing (WGS) dataset from blood samples of 38 MIDD patients with the m.3243A > G mutation belonging to 10 pedigrees, by developing a Kinship‐graph convolutional network approach, called Ki‐GCN, integrated with the conventional genome‐wide association study (GWAS) methods. Results We identified eight protective alleles in the nuclear genome that have protective effects against the onset of MIDD, related deafness, and also type 2 diabetes. Based on these eight protective alleles, we constructed an effective logistic regression model to predict the early or late onset of MIDD patients. Conclusions There are protective alleles in the nuclear genome that are associated with the onset‐age of MIDD patients and might also provide protective effects on the deafness derived from MIDD patients.

Keywords

• Genetics and pathogenesis
• Machine learning
• Maternally inherited diabetes and deafness