Abstract
Genome-wide association study (GWAS) is widely used to identify genes involved in plants, animals and human complex traits. Generally, the identified SNP is not necessarily the causal variant, but it is rather in linkage disequilibrium (LD). One key challenge for GWAS results interpretation is to rapidly identify causal genes and provide profound evidence on how they affect the trait. Researches want to identify candidate causal variants from the most significant SNPs of GWAS in any species and on their local computer, while to complete these tasks are to be time-consuming, laborious and prone to errors and omission. To our knowledge, so far there is no tool available to solve the challenge for GWAS data very quickly. Based on the standard VCF (variant call format) format, CandiHap is developed to fast preselection candidate causal SNPs and gene(s) from GWAS by integrating LD result, SNP annotation, haplotype analysis and traits statistics of haplotypes. Investigators can specify genes or linkage regions based on GWAS results, linkage disequilibrium (LD), and predicted candidate causal gene(s). It supported Windows, Mac and Linux computers and servers in graphical interface and command line, and applied to any other plant, animal or bacteria species. The source code of CandiHap tool is freely available at https://github.com/xukaili/CandiHap
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