Resistance to Multiple Temperate and Tropical Stem and Sheath Diseases of Rice

Stem rot and aggregated sheath spot are the two major stem and sheath diseases affecting rice (Oryza sativa L.) in temperate areas. A third fungal disease, sheath blight, is a major disease in tropical areas. Resistance to these diseases is a key objective in rice breeding programs but phenotyping is challenged by the confounding effects of phenological and morphological traits such as flowering time (FT) and plant height (PH). This study sought to identify quantitative trait loci (QTL) for resistance to these three diseases after removing the confounding effects of FT and PH. Two populations of advanced breeding germplasm, one with 316 tropical japonica and the other with 325 indica genotypes, were evaluated in field and greenhouse trials for resistance to the diseases. Phenotypic means for field and greenhouse disease resistance, adjusted by FT and PH, were analyzed for associations with 29,000 single nucleotide polymorphisms (SNPs) in tropical japonica and 50,000 SNPs in indica. A total of 29 QTL were found for resistance that were not associated with FT or PH. Multilocus models with selected resistance-associated SNPs were fitted for each disease to estimate their effects on the other diseases. A QTL on chromosome 9 accounted for more than 15% of the phenotypic variance for the three diseases. When resistance-associated SNPs at this locus from both the tropical japonica and indica populations were incorporated into the model, resistance was improved for all three diseases with little impact on FT and PH.

Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America

Multi-environment multi-QTL mixed models were used in a GWAS context to identify QTL for disease resistance. The use of mega-environments aided the interpretation of environment-specific and general QTL. Diseases represent a major constraint for barley (Hordeum vulgare L.) production in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f.sp. hordei) and leaf rust (caused by Puccinia hordei) are three of the most important diseases that affect the crop in the region. Since fungicide application is not an economically or environmentally sound solution, the development of durably resistant varieties is a priority for breeding programs. Therefore, new resistance sources are needed. The objective of this work was to detect genomic regions associated with field level plant resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Disease severities measured in multi-environment trials across the Americas and 1,096 SNPs in a population of 360 genotypes were used to identify genomic regions associated with disease resistance. Optimized experimental design and spatial modeling were used in each trial to estimate genotypic means. Genome-Wide Association Mapping (GWAS) in each environment was used to detect Quantitative Trait Loci (QTL). All significant environment-specific QTL were subsequently included in a multi-environment-multi-QTL (MEMQ) model. Geographical origin and inflorescence type were the main determinants of population structure. Spot blotch severity was low to intermediate while leaf and stripe rust severity was high in all environments. Mega-environments were defined by locations for spot blotch and leaf rust. Significant marker-trait associations for spot blotch (9 QTL), leaf (6 QTL) and stripe rust (7 QTL) and both global and environment-specific QTL were detected that will be useful for future breeding efforts.

A mixed model QTL analysis for sugarcane multiple-harvest-location trial data

Sugarcane-breeding programs take at least 12 years to develop new commercial cultivars. Molecular markers offer a possibility to study the genetic architecture of quantitative traits in sugarcane, and they may be used in marker-assisted selection to speed up artificial selection. Although the performance of sugarcane progenies in breeding programs are commonly evaluated across a range of locations and harvest years, many of the QTL detection methods ignore two- and three-way interactions between QTL, harvest, and location. In this work, a strategy for QTL detection in multi-harvest-location trial data, based on interval mapping and mixed models, is proposed and applied to map QTL effects on a segregating progeny from a biparental cross of pre-commercial Brazilian cultivars, evaluated at two locations and three consecutive harvest years for cane yield (tonnes per hectare), sugar yield (tonnes per hectare), fiber percent, and sucrose content. In the mixed model, we have included appropriate (co)variance structures for modeling heterogeneity and correlation of genetic effects and non-genetic residual effects. Forty-six QTLs were found: 13 QTLs for cane yield, 14 for sugar yield, 11 for fiber percent, and 8 for sucrose content. In addition, QTL by harvest, QTL by location, and QTL by harvest by location interaction effects were significant for all evaluated traits (30 QTLs showed some interaction, and 16 none). Our results contribute to a better understanding of the genetic architecture of complex traits related to biomass production and sucrose content in sugarcane.

Look before you leap: a new approach to mapping QTL

In this paper, we present an innovative and powerful approach for mapping quantitative trait loci (QTL) in experimental populations. This deviates from the traditional approach of (composite) interval mapping which uses a QTL profile to simultaneously determine the number and location of QTL. Instead, we look before we leap by employing separate detection and localization stages. In the detection stage, we use an iterative variable selection process coupled with permutation to identify the number and synteny of QTL. In the localization stage, we position the detected QTL through a series of one-dimensional interval mapping scans. Results from a detailed simulation study and real analysis of wheat data are presented. We achieve impressive increases in the power of QTL detection compared to composite interval mapping. We also accurately estimate the size and position of QTL. An R library, DLMap, implements the methods described here and is freely available from CRAN ( http://cran.r-project.org/ ).

The analysis of QTL by simultaneous use of the full linkage map

An extension of interval mapping is presented that incorporates all intervals on the linkage map simultaneously. The approach uses a working model in which the sizes of putative QTL for all intervals across the genome are random effects. An outlier detection method is used to screen for possible QTL. Selected QTL are subsequently fitted as fixed effects. This screening and selection approach is repeated until the variance component for QTL sizes is not statistically significant. A comprehensive simulation study is conducted in which map uncertainty is included. The proposed method is shown to be superior to composite interval mapping in terms of power of detection of QTL. There is an increase in the rate of false positive QTL detected when using the new approach, but this rate decreases as the population size increases. The new approach is much simpler computationally. The analysis of flour milling yield in a doubled haploid population illustrates the improved power of detection of QTL using the approach, and also shows how vital it is to allow for sources of non-genetic variation in the analysis.

A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize

Complex quantitative traits of plants as measured on collections of genotypes across multiple environments are the outcome of processes that depend in intricate ways on genotype and environment simultaneously. For a better understanding of the genetic architecture of such traits as observed across environments, genotype-by-environment interaction should be modeled with statistical models that use explicit information on genotypes and environments. The modeling approach we propose explains genotype-by-environment interaction by differential quantitative trait locus (QTL) expression in relation to environmental variables. We analyzed grain yield and grain moisture for an experimental data set composed of 976 F(5) maize testcross progenies evaluated across 12 environments in the U.S. corn belt during 1994 and 1995. The strategy we used was based on mixed models and started with a phenotypic analysis of multi-environment data, modeling genotype-by-environment interactions and associated genetic correlations between environments, while taking into account intraenvironmental error structures. The phenotypic mixed models were then extended to QTL models via the incorporation of marker information as genotypic covariables. A majority of the detected QTL showed significant QTL-by-environment interactions (QEI). The QEI were further analyzed by including environmental covariates into the mixed model. Most QEI could be understood as differential QTL expression conditional on longitude or year, both consequences of temperature differences during critical stages of the growth.

QTL analysis of malting quality traits in two barley populations

Selection for malting quality traits is a major breeding objective for barley breeding programs. With molecular markers linked to loci affecting these traits, this selection can be undertaken at an earlier stage of the breeding program than is possible using conventional tests. Quantitative trait loci (QTLs) associated with malting quality traits were mapped in 2 populations derived from parents with elite malting quality. Progeny from an Arapiles/Franklin population grown in 4 environments and an Alexis/Sloop population grown in 5 environments were tested for grain protein percentage, α-amylase activity, diastatic power, hot water extract, wort viscosity, wort β-glucan, β-glucanase, and free α-amino acids. QTL analysis was performed using a one-stage approach, which allowed for modelling of spatial variation in the field, and in each phase of the malting quality analysis in the laboratory. QTLs for malting quality traits were detected on all chromosomes and for both populations. Few of these QTLs were significant in all of the environments, indicating that QTL × environment interactions were important. There were many coincident QTLs for traits that are expected to be related such as diastatic power and α-amylase activity, wort β-glucan and wort viscosity and for some traits that are not expected to be related such as hot water extract and malt viscosity.

Joint modeling of additive and non-additive genetic line effects in single field trials

A statistical approach is presented for selection of best performing lines for commercial release and best parents for future breeding programs from standard agronomic trials. The method involves the partitioning of the genetic effect of a line into additive and non-additive effects using pedigree based inter-line relationships, in a similar manner to that used in animal breeding. A difference is the ability to estimate non-additive effects. Line performance can be assessed by an overall genetic line effect with greater accuracy than when ignoring pedigree information and the additive effects are predicted breeding values. A generalized definition of heritability is developed to account for the complex models presented.

Genetic characterisation of dough rheological properties in a wheat doubled haploid population: additive genetic effects and epistatic interactions

Doubled haploid lines (n = 160) from a cross between wheat cultivars 'Cranbrook' (high dough extensibility) and 'Halberd' (low dough extensibility) were grown at three Australian locations. The parents differ at all high- and low-molecular-weight glutenin loci. Dough rheological parameters were measured using small-scale testing procedures, and quantitative trait locus (QTL) mapping procedures were carried out using an existing well-saturated genetic linkage map for this cross. Genetic parameters were estimated using three software packages: QTLCartographer, Epistat and Genstat. Results indicated that environmental factors are a major determinant of dough extensibility across the three trial sites, whereas genotypic factors are the major determinants of dough strength. Composite interval mapping analysis across the 21 linkage groups revealed that as expected, the main additive QTLs for dough rheological properties are located at the high- and low-molecular-weight glutenin loci. A new QTL on chromosome 5A for M-extensibility (a mixograph-estimated measure of extensibility) was detected. Analysis of epistatic interactions revealed that there were significant conditional epistatic interactions related with the additive effects of glutenin loci on dough rheological properties. Therefore, the additive genetic effects of glutenins on dough rheological properties are conditional upon the genetic background of the wheat line. The molecular basis of the interactions with the glutenin loci may be via proteins that modify or alter the gluten protein matrix or variations in the expression level of the glutenin genes. Reverse-phase high performance liquid chromatography analysis of the molar number of individual glutenin subunits across the population showed that certain conditional epistases resulted in increased expression of the affected glutenin. The epistatic interactions detected in this study provide a possible explanation of the variable genetic effects of some glutenins on quality attributes in different genetic backgrounds and provide essential information for the accurate prediction of glutenin related variance in marker-assisted wheat breeding.

Genetic and in silico comparative mapping of the polyphenol oxidase gene in bread wheat (Triticum aestivum L.)

Polyphenol oxidases (PPOs) are involved in the time-dependent darkening and discolouration of Asian noodles and other wheat end products. In this study, a doubled haploid (DH) population derived from Chara (moderately high PPO activity)/WW2449 (low PPO activity) was screened for PPO activity based on L-DOPA and L-tyrosine assays using whole seeds. Both these assays were significantly genetically correlated (r = 0.91) in measuring the PPO activity in this DH population. Quantitative trait loci (QTLs) analysis utilising a skeleton map enabled us to identify a major QTL controlling PPO activity based on L-DOPA and L-tyrosine on the long arm of chromosome 2A. The simple sequence repeat (SSR) marker GWM294b explained over 82% of the line mean phenotypic variation from samples collected in both 2000 and 2003. Four SSR markers were validated for PPO linkage in genetically diverse backgrounds and proven to correctly predict the PPO activity in more than 92% of wheat lines. Physical mapping using deletion lines of Chinese Spring has confirmed the location of the GWM294b, GWM312 and WMC170 on chromosome 2AL, between deletion breakpoints 2AL-C to 0.85. In order to identify functional gene markers, data searches for alignments between rice BAC/PAC clones assembled on chromosome 1 and 4, chromosome 7, and (1) the wheat expressed sequence tags mapped in deletion bin (2AL-C to 0.85) and (2) the coding sequence of a previously cloned wheat PPO gene were made and found significant sequence similarities with the PPO gene or common central domain of tyrosinase. Available PPO gene sequences in the National Centre for Biotechnology Information (NCBI) database have revealed that there is a significant molecular diversity at the nucleotide and amino acid level in the wheat PPO genes.

Statistical tests for QTL and QTL-by-environment effects in segregating populations derived from line crosses

Quantitative trait locus (QTL) studies in plants frequently employ phenotypic data on a population of lines (doubled haploid lines, recombinant inbred lines, etc.) tested in multiple environments. An important feature of such data is the genetic correlation among observations on the same genotype in different environments. Detection of QTL-by-environment interaction requires tests which take this correlation into account. In this article, a comparison was made of the properties of several such tests by means of simulation. The results indicate that a split-plot analysis of variance (ANOVA), being an approximate method, tends to be too liberal under departures from the Huynh-Feldt condition. A standard two-way ANOVA, which ignores genetic correlation, yields inappropriate tests and should be avoided. In contrast, mixed model approaches as well as univariate and multivariate repeated-measures ANOVA yield valid results. This supports the use of a flexible mixed model framework in more complex settings, which are difficult to tackle by repeated-measures ANOVA. .