An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases
Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome...
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022002914 |
| _version_ | 1797978228753694720 |
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| author | Yangyang Yuan Liubin Zhang Qihan Long Hui Jiang Miaoxin Li |
| author_facet | Yangyang Yuan Liubin Zhang Qihan Long Hui Jiang Miaoxin Li |
| author_sort | Yangyang Yuan |
| collection | DOAJ |
| description | Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome is often discouraged by the high dimension burden. Thus, numerous digenic interactions are yet to be identified for many diseases. Here, we propose a Digenic Interaction Effect Predictor (DIEP), an accurate machine-learning approach to identify the genome-wide pathogenic coding gene pairs with digenic interaction effects. This approach achieved high accuracy and sensitivity in independent testing datasets, outperforming another gene-level digenic predictor (DiGePred). DIEP was also able to discriminate digenic interaction effect from bi-locus effects dual molecular diagnosis (pseudo-digenic). Using DIEP, we provided a valuable resource of genome-wide digenic interactions and demonstrated the enrichment of the digenic interaction effect in Mendelian and Oligogenic diseases. Therefore, DIEP will play a useful role in facilitating the genomic mapping of interactive causal genes for human diseases. |
| first_indexed | 2024-04-11T05:19:39Z |
| format | Article |
| id | doaj.art-8be19c252d714cc794c64f3bc610c180 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:19:39Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-8be19c252d714cc794c64f3bc610c1802022-12-24T04:53:23ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012036393652An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseasesYangyang Yuan0Liubin Zhang1Qihan Long2Hui Jiang3Miaoxin Li4Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China; Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, ChinaProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China; Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, ChinaProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China; Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, ChinaProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China; Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, ChinaProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China; Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, China; Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, China; Corresponding author at: Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome is often discouraged by the high dimension burden. Thus, numerous digenic interactions are yet to be identified for many diseases. Here, we propose a Digenic Interaction Effect Predictor (DIEP), an accurate machine-learning approach to identify the genome-wide pathogenic coding gene pairs with digenic interaction effects. This approach achieved high accuracy and sensitivity in independent testing datasets, outperforming another gene-level digenic predictor (DiGePred). DIEP was also able to discriminate digenic interaction effect from bi-locus effects dual molecular diagnosis (pseudo-digenic). Using DIEP, we provided a valuable resource of genome-wide digenic interactions and demonstrated the enrichment of the digenic interaction effect in Mendelian and Oligogenic diseases. Therefore, DIEP will play a useful role in facilitating the genomic mapping of interactive causal genes for human diseases.http://www.sciencedirect.com/science/article/pii/S2001037022002914Genetic interactionDigenic interaction effectMachine learningPathogenic gene pairsEnrichmentGenomic mapping |
| spellingShingle | Yangyang Yuan Liubin Zhang Qihan Long Hui Jiang Miaoxin Li An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases Computational and Structural Biotechnology Journal Genetic interaction Digenic interaction effect Machine learning Pathogenic gene pairs Enrichment Genomic mapping |
| title | An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases |
| title_full | An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases |
| title_fullStr | An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases |
| title_full_unstemmed | An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases |
| title_short | An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases |
| title_sort | accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases |
| topic | Genetic interaction Digenic interaction effect Machine learning Pathogenic gene pairs Enrichment Genomic mapping |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022002914 |
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