Predicting disease genes for familial dilated cardiomyopathy based on the codon usage bias

Familial dilated cardiomyopathy （FDC） is a common monogenic disease mostly with autosomal dominant inheritance. Fifteen different loci for autosomal dominant FDC have been mapped; however, only eight FDC genes have been found, and it is still a big challenge to identify additional seven FDC genes in their chromosomal regions. We found that the codon usage frequencies in most of known FDC gene sequences are consistently biased, and significantly different from the average codon usage frequencies of human genes. This unique feature of codon usage was used to develop a novel approach to predicting FDC genes. Leave-oneout cross-validation results demonstrate that this approach can effectively detect FDC genes from numbers of genes in their chromosomal regions. Another advantage of this approach is that it is solely based on DNA sequences and therefore has the ability to identify potential FDC genes whose functions are completely unknown. Further, this approach has been used to analyze the seven FDC loci in which the FDC genes are still unknown. Both the detailed prediction results and the prediction program are available at http：// infosci.hust.edu.cn, which might provide help for relevant experimental researches to find new FDC genes.

Predicting disease genes for familial dilated cardiomyopathy based on the codon usage bias

Familial dilated cardiomyopathy (FDC) is a common monogenic disease mostly with autosomal dominant inheritance. Fifteen different loci for autosomal dominant FDC have been mapped; however, only eight FDC genes have been found, and it is still a big challenge to identify ad-ditional seven FDC genes in their chromosomal regions. We found that the codon usage frequencies in most of known FDC gene sequences are consistently biased, and significantly different from the average codon usage frequencies of human genes. This unique feature of codon usage was used to de-velop a novel approach to predicting FDC genes. Leave-one- out cross-validation results demonstrate that this approach can effectively detect FDC genes from numbers of genes in their chromosomal regions. Another advantage of this ap-proach is that it is solely based on DNA sequences and there-fore has the ability to identify potential FDC genes whose functions are completely unknown. Further, this approach has been used to analyze the seven FDC loci in which the FDC genes are still unknown. Both the detailed prediction results and the prediction program are available at http:// infosci.hust.edu.cn, which might provide help for relevant experimental researches to find new FDC genes.