Identification Of Candidate Genes In Forward Genetic Studies

With the development of sequencing technology,the accuracy of high-throughput sequencing is increasing and the cost is decreasing year by year.The related applications are more and more widely.Based on the high-throughput sequencing technology,this paper presents a method for identifying candidate genes for family genetic diseases.The actual operation needs to set the proper parameters,implicit assumptions,and the candidate gene is the suppressor or activator hypothesis and other issues.It is challenging for researchers to choose the better parameters to get the results of the analysis quickly and reasonably.In order to solve this problem,the software(GIPS)based on the second generation sequencing technology came into being.It provided four guiding parameters for the researchers and successfully applied them to rice sequencing data.In this paper,we consider all the possible occurrences of genetic diseases in families.,and add new filtering algorithms for family analysis,interfaces of annotation software:annovar,Manhattan map drawing function and the definition and formula improvement of the underlying mutation rate algorithm to further increase the practicality,scalability and accuracy of the software.And to improve operational efficiency,the introduction of a quick access to candidate gene list of the new command line.According to the second generation GIPS software optimization,firstly,it is discussed that there may be a variety of situations that may exist in the genetic disease of the family,such as the dominance and recessive genes of the disease to be studied or the candidate gene is a suppressor or an activator.We screen for different situations by the genotypes of SNP in the locus.For more intuitive to show the degree of relevance between all genes in genome and phenotype,we can draw the Manhattan plot based the significance data of each gene.It can also directly reflect the number of candidate genes screened by threshold.By improving the algorithm of background mutation rate,we get a more accurate criteria and we also make a detailed comparison of the two background mutation rates.In order to improve the algorithm from a biological point of view,we can also integrate different types of scoring software,from different angles(such as the degree of sequence conservation,physical and chemical properties,etc.)to guide the selection of SNP.To this end,a new generation of GIPS on the basis of the new scoring software interface,these new features are successfully applied in LHON,deafness and other family genetic disease data.Website:http://www.plantphysiol.org/content/170/4/1929...