Current Progress on Statistical Methods for Mapping Quantitative Trait Loci from Inbred Line Crosses

Multiple interval QTL mapping and searching for PSTOL1 homologs associated with root morphology, biomass accumulation and phosphorus content in maize seedlings under low-P

BACKGROUND

Modifications in root morphology are important strategies to maximize soil exploitation under phosphorus starvation in plants. Here, we used two multiple interval models to map QTLs related to root traits, biomass accumulation and P content in a maize RIL population cultivated in nutrient solution. In addition, we searched for putative maize homologs to PSTOL1, a gene responsible to enhance early root growth, P uptake and grain yield in rice and sorghum.

RESULTS

Based on path analysis, root surface area was the root morphology component that most strongly contributed to total dry weight and to P content in maize seedling under low-P availability. Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) were combined and revealed 13 genomic regions significantly associated with the target traits in a complementary way. The phenotypic variances explained by all QTLs and their epistatic interactions using MT-MIM (23.4 to 35.5 %) were higher than in previous studies, and presented superior statistical power. Some of these QTLs were coincident with QTLs for root morphology traits and grain yield previously mapped, whereas others harbored ZmPSTOL candidate genes, which shared more than 55 % of amino acid sequence identity and a conserved serine/threonine kinase domain with OsPSTOL1. Additionally, four ZmPSTOL candidate genes co-localized with QTLs for root morphology, biomass accumulation and/or P content were preferentially expressed in roots of the parental lines that contributed the alleles enhancing the respective phenotypes.

CONCLUSIONS

QTL mapping strategies adopted in this study revealed complementary results for single and multiple traits with high accuracy. Some QTLs, mainly the ones that were also associated with yield performance in other studies, can be good targets for marker-assisted selection to improve P-use efficiency in maize. Based on the co-localization with QTLs, the protein domain conservation and the coincidence of gene expression, we selected novel maize genes as putative homologs to PSTOL1 that will require further validation studies.

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

Background

Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software.

Results

Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits.

Conclusions

The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.

Composite interval mapping and multiple interval mapping: procedures and guidelines for using Windows QTL Cartographer

Tremendous progress has been made in recent years on developing statistical methods for mapping quantitative trait loci (QTL) from crosses of inbred lines. In this chapter, we provide an introduction of composite interval mapping and multiple interval mapping methods for mapping QTL from inbred line crosses and also detailed instructions to perform the analyses in Windows QTL Cartographer. For each method, we discuss the meaning of each option in the analysis procedures and how to understand and interpret the mapping results through a work-out example.