Efficient distributed SNP selection by a Modified Binary Flower Pollination Algorithm

Porcine Single Nucleotide Polymorphisms (SNPs-certain pieces of nucleotide in a DNA sequence) can be indirectly associated with traits of an individual pig, like its meat quality or resistance to common diseases. It is most desirable to obtain a smallest number of most significant SNPs in genomics research, and several computer classification algorithms have been used to do so. For instance, for breed classification, one needs to obtain a set of a much smaller number of significant SNPs than that of the entire SNP data set. This study attempted to find such significant porcine SNPs by using computational feature selection and classification methods. In a preliminary trial, a binary flower pollination algorithm (BFPA) was used and shown not to able to reduce the number of selected SNPs to a sufficiently low number. Therefore, to achieve our objective, we developed a vertically distributed feature selection method incorporating a modified BFPA and a support vector machine classifier for selecting significant porcine SNPs. The developed method was evaluated and compared against four baseline methods. It provided the smallest average number of significant SNPs (128.40) that resulted in 94.57% classification accuracy. This and other findings in this study may directly benefit researchers in the bioinformatics field in their effort to map SNPs.