Soybean mosaic virus （SMV）, a member of the genus Potyvirus, is a major pathogen of soybean plants in China, and 16 SMV strains have been identified nationwide based on a former detailed SMV classification system. As the P3 gene is thought to be involved in viral replication, systemic infection, pathogenicity, and overcoming resistance, knowledge of the P3 gene sequences of SMV and other potyviruses would be useful in efforts to know the genetic relationships among them and control the disease. P3 gene sequences were obtained from representative isolates of the above-mentioned 16 SMV strains and were compared with other SMV strains and 16 Potyvirus species from the National Center for Biotechnology GenBank database. The P3 genes from the 16 SMV isolates are composed of 1041 nucleotides, encoding 347 amino acids, and share 90.7-100% nucleotide （NT） sequence identities and 95.1-100% amino acid （AA） sequence identities. The P3 coding regions of the 16 SMV isolates share high identities （92.4-98.9% NT and 96.0-100% AA） with the reported Korean isolates, followed by the USA isolates （88.5-97.9% NT and 91.4-98.6% AA）, and share low identities （80.5-85.2% NT and 82.1-84.7% AA） with the reported HZ 1 and P isolates from Pinellia ternata. The sequence identities of the P3 genes between SMV and the 16 potyviruses varied from 44.4 to 81.9% in the NT sequences and from 21.4 to 85.3% in the AA sequences, respectively. Among them, SMV was closely related to Watermelon mosaic virus （WMV）, with 76.0-81.9% NT and 77.5-85.3% AA identities. In addition, the SMV isolates and potyvirus species were clustered into six distinct groups. All the SMV strains isolated from soybean were clustered in Group I, and the remaining species were clustered in other groups. A multiple sequence alignment analysis of the C-terminal regions indicated that the P3 genes within a species were highly conserved, whereas those among species were relatively variable.
Advances on methods for mapping quantitative trait loci (QTL) are firstly summarized. Then, some new methods, including mapping multiple QTL, fine mapping of QTL, and mapping QTL for dynamic traits, are mainly described. Finally, some future prospects are proposed, including how to dig novel genes in the germplasm resource, map expression QTL (eQTL) by the use of all markers, phenotypes and micro-array data, identify QTL using genetic mating designs and detect viability loci. The purpose is to direct plant geneticists to choose a suitable method in the inheritance analysis of quantitative trait and in search of novel genes in germplasm resource so that more potential genetic information can be uncovered.
