Comparison of statistical models for nested association mapping in rapeseed (Brassica napus L.) through computer simulations.

BACKGROUND: Rapeseed (Brassica napus L.) is an important oilseed crop throughout the world, serving as source for edible oil and renewable energy. Development of nested association mapping (NAM) population and methods is of importance for quantitative trait locus (QTL) mapping in rapeseed. The objectives of the research were to compare the power of QTL detection 1- ß(∗) (ß(∗) is the empirical type II error rate) (i) of two mating designs, double haploid (DH-NAM) and backcross (BC-NAM), (ii) of different statistical models, and (iii) for different genetic situations. RESULTS: The computer simulations were based on the empirical data of a single nucleotide polymorphism (SNP) set of 790 SNPs from 30 sequenced conserved genes of 51 accessions of world-wide diverse B. napus germplasm. The results showed that a joint composite interval mapping (JCIM) model had significantly higher power of QTL detection than a single marker model. The DH-NAM mating design showed a slightly higher power of QTL detection than the BC-NAM mating design. The JCIM model considering QTL effects nested within subpopulations showed higher power of QTL detection than the JCIM model considering QTL effects across subpopulations, when examing a scenario in which there were interaction effects by a few QTLs interacting with a few background markers as well as a scenario in which there were interaction effects by many QTLs (≥ 25) each with more than 10 background markers and the proportion of total variance explained by the interactions was higher than 75 %. CONCLUSIONS: The results of our study support the optimal design as well as analysis of NAM populations, especially in rapeseed.

Multiplexed digital gene expression analysis for genetical genomics in large plant populations.

Digital gene expression (DGE) analysis is a cost-effective method for large-scale quantitative transcriptome analysis using second generation sequencing. Here we describe how adaptation of DGE with barcode indexing in large segregating plant populations of over 100 genotypes can be applied for successful expression QTL (eQTL) and gene expression network analysis to develop transcript-based markers for breeding.