Jatropha curcas is an important perennial, drought tolerant plant that has been identified as a potential biodiesel crop. We report here the hybrid de novo genome assembly of J. curcas generated using Illumina and PacBio sequencing technologies, and identification of quantitative loci for Jatropha Mosaic Virus (JMV) resistance. In this study, we generated scaffolds of 265.7 Mbp in length, which correspond to 84.8% of the gene space, using Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis. Additionally, 96.4% of predicted protein-coding genes were captured in RNA sequencing data, which reconfirms the accuracy of the assembled genome. The genome was utilized to identify 12,103 dinucleotide simple sequence repeat (SSR) markers, which were exploited in genetic diversity analysis to identify genetically distinct lines. A total of 207 polymorphic SSR markers were employed to construct a genetic linkage map for JMV resistance, using an interspecific F2 mapping population involving susceptible J. curcas and resistant Jatropha integerrima as parents. Quantitative trait locus (QTL) analysis led to the identification of three minor QTLs for JMV resistance, and the same has been validated in an alternate F2 mapping population. These validated QTLs were utilized in marker-assisted breeding for JMV resistance. Comparative genomics of oil-producing genes across selected oil producing species revealed 27 conserved genes and 2986 orthologous protein clusters in Jatropha. This reference genome assembly gives an insight into the understanding of the complex genetic structure of Jatropha, and serves as source for the development of agronomically improved virus-resistant and oil-producing lines.
This paper presents a combined principal component analysis-regression analysis (PCA-RA) model to study the effects of independent variables namely exposed branches (E.B), yield bearing branches (YBB), total inflorescence (TIFL), average fruits per cluster (AFC) and average females per cluster (AFEC) on total fruit count (TFC). In this section, a multiple linear regression is applied to develop a forecasting model for total fruit count (TFC). Multicollinearity often causes a huge explanatory problem in multiple linear regression analysis. In presence of multi-collinearity the ordinary least squares (OLS) estimators are inaccurately estimated and the coefficient of independent variables appearing to have the wrong sign. In this paper, the multi-collinearity was detected by using observing correlation matrix and variance influence factor (VIF). This study revealed that principal component regression (PCR) method facilitates to solve the multi-collinearity problem.
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