Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification
Abstract Background Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is or...
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2361-5 |
| _version_ | 1818146520039424000 |
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| author | Xiangdong Zhou Keith C. C. Chan |
| author_facet | Xiangdong Zhou Keith C. C. Chan |
| author_sort | Xiangdong Zhou |
| collection | DOAJ |
| description | Abstract Background Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective. Results Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs. Conclusion The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits. |
| first_indexed | 2024-12-11T12:20:39Z |
| format | Article |
| id | doaj.art-cc787d85a6004f1dbaa51cb36175759f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T12:20:39Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-cc787d85a6004f1dbaa51cb36175759f2022-12-22T01:07:32ZengBMCBMC Bioinformatics1471-21052018-09-0119111310.1186/s12859-018-2361-5Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classificationXiangdong Zhou0Keith C. C. Chan1College of Mathematics and Computer Science, Fuzhou UniversityDepartment of Computing, the Hong Kong Polytechnic UniversityAbstract Background Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective. Results Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs. Conclusion The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.http://link.springer.com/article/10.1186/s12859-018-2361-5Quantitative traitsGene-gene interactionsMultifactor dimensionality reductionOrdinal traitsFuzzy accuracy |
| spellingShingle | Xiangdong Zhou Keith C. C. Chan Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification BMC Bioinformatics Quantitative traits Gene-gene interactions Multifactor dimensionality reduction Ordinal traits Fuzzy accuracy |
| title | Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification |
| title_full | Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification |
| title_fullStr | Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification |
| title_full_unstemmed | Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification |
| title_short | Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification |
| title_sort | detecting gene gene interactions for complex quantitative traits using generalized fuzzy classification |
| topic | Quantitative traits Gene-gene interactions Multifactor dimensionality reduction Ordinal traits Fuzzy accuracy |
| url | http://link.springer.com/article/10.1186/s12859-018-2361-5 |
| work_keys_str_mv | AT xiangdongzhou detectinggenegeneinteractionsforcomplexquantitativetraitsusinggeneralizedfuzzyclassification AT keithccchan detectinggenegeneinteractionsforcomplexquantitativetraitsusinggeneralizedfuzzyclassification |