The genetic basis of population fecundity prediction across multiple field populations of Nilaparvata lugens - Zhong Xiang Sun; Yi Fan Zhai; Jian Qing Zhang; Kui Kang; Jing Heng Cai; Yonggui Fu; Jie Qi Qiu; Jia Wei Shen; Wen Qing Zhang (Molecular Ecology, 2015)
Identifying the molecular markers for complex quantitative traits in natural populations promises to provide novel insight into genetic mechanisms of adaptation and to aid in forecasting population dynamics. In this study, we investigated single nucleotide polymorphisms ( SNP s) using candidate gene approach from high‐ and low‐fecundity populations of the brown planthopper ( BPH ) Nilaparvata lugens Stål (Hemiptera: Delphacidae) divergently selected for fecundity. We also tested whether the population fecundity can be predicted by a few SNP s. Seven genes ( ACE , fizzy, HMGCR , LpR, Sxl, Vg and VgR ) were inspected for SNP s in N. lugens , which is a serious insect pest of rice. By direct sequencing of the complementary DNA and promoter sequences of these candidate genes, 1033 SNP s were discovered within high‐ and low‐fecundity BPH populations. A panel of 121 candidate SNP s were selected and genotyped in 215 individuals from 2 laboratory populations ( HFP and LFP ) and 3 field populations ( GZP , SGP and ZSP ). Prior to association tests, population structure and linkage disequilibrium ( LD ) among the 3 field populations were analysed. The association results showed that 7 SNP s were significantly associated with population fecundity in BPH . These significant SNP s were used for constructing general liner models with stepwise regression. The best predictive model was composed of 2 SNP s ( ACE ‐862 and VgR ‐816 ) with very good fitting degree. We found that 29% of the phenotypic variation in fecundity could be accounted for by only two markers. Using two laboratory populations and a complete independent field population, the predictive accuracy was 84.35–92.39%. The predictive model provides an efficient molecular method to predict BPH fecundity of field populations and provides novel insights for insect population management.