Power of mixed-model QTL mapping from phenotypic, pedigree and marker data in self-pollinated crops

A genome-wide association study of malting quality across eight U.S. barley breeding programs

We report malt quality QTLs relevant to breeding with greater precision than previous mapping studies. The distribution of favorable alleles suggests strategies for marker-assisted breeding and germplasm exchange. This study leverages the breeding data of 1,862 barley breeding lines evaluated in 97 field trials for genome-wide association study of malting quality traits in barley. The mapping panel consisted of six-row and two-row advanced breeding lines from eight breeding populations established at six public breeding programs across the United States. A total of 4,976 grain samples were subjected to micro-malting analysis and mapping of nine quality traits was conducted with 3,072 SNP markers distributed throughout the genome. Association mapping was performed for individual breeding populations and for combined six-row and two-row populations. Only 16% of the QTL we report here had been detected in prior bi-parental mapping studies. Comparison of the analyses of the combined two-row and six-row panels identified only two QTL regions that were common to both. In total, 108 and 107 significant marker-trait associations were identified in all six-row and all two-row breeding programs, respectively. A total of 102 and 65 marker-trait associations were specific to individual six-row and two-row breeding programs, respectively indicating that most marker-trait associations were breeding population specific. Combining datasets from different breeding program resulted in both the loss of some QTL that were apparent in the analyses of individual programs and the discovery of new QTL not identified in individual programs. This suggests that simply increasing sample size by pooling samples with different breeding history does not necessarily increase the power to detect associations. The genetic architecture of malting quality and the distribution of favorable alleles suggest strategies for marker-assisted selection and germplasm exchange.

Construction and application for QTL analysis of a Restriction Site Associated DNA (RAD) linkage map in barley

BACKGROUND

Linkage maps are an integral resource for dissection of complex genetic traits in plant and animal species. Canonical map construction follows a well-established workflow: an initial discovery phase where genetic markers are mined from a small pool of individuals, followed by genotyping of selected mapping populations using sets of marker panels. A newly developed sequence-based marker technology, Restriction site Associated DNA (RAD), enables synchronous single nucleotide polymorphism (SNP) marker discovery and genotyping using massively parallel sequencing. The objective of this research was to assess the utility of RAD markers for linkage map construction, employing barley as a model system. Using the published high density EST-based SNP map in the Oregon Wolfe Barley (OWB) mapping population as a reference, we created a RAD map using a limited set of prior markers to establish linakge group identity, integrated the RAD and prior data, and used both maps for detection of quantitative trait loci (QTL).

RESULTS

Using the RAD protocol in tandem with the Illumina sequence by synthesis platform, a total of 530 SNP markers were identified from initial scans of the OWB parental inbred lines--the "dominant" and "recessive" marker stocks--and scored in a 93 member doubled haploid (DH) mapping population. RAD sequence data from the structured population was converted into allele genotypes from which a genetic map was constructed. The assembled RAD-only map consists of 445 markers with an average interval length of 5 cM, while an integrated map includes 463 RAD loci and 2383 prior markers. Sequenced RAD markers are distributed across all seven chromosomes, with polymorphic loci emanating from both coding and noncoding regions in the Hordeum genome. Total map lengths are comparable and the order of common markers is identical in both maps. The same large-effect QTL for reproductive fitness traits were detected with both maps and the majority of these QTL were coincident with a dwarfing gene (ZEO) and the VRS1 gene, which determines the two-row and six-row germplasm groups of barley.

CONCLUSIONS

We demonstrate how sequenced RAD markers can be leveraged to produce high quality linkage maps for detection of single gene loci and QTLs. By combining SNP discovery and genotyping into parallel sequencing events, RAD markers should be a useful molecular breeding tool for a range of crop species. Expected improvements in cost and throughput of second and third-generation sequencing technologies will enable more powerful applications of the sequenced RAD marker system, including improvements in de novo genome assembly, development of ultra-high density genetic maps and association mapping.

Family-based mapping of quantitative trait loci in plant breeding populations with resistance to Fusarium head blight in wheat as an illustration

Traditional quantitative trait loci (QTL) mapping approaches are typically based on early or advanced generation analysis of bi-parental populations. A limitation associated with this methodology is the fact that mapping populations rarely give rise to new cultivars. Additionally, markers linked to the QTL of interest are often not immediately available for use in breeding and they may not be useful within diverse genetic backgrounds. Use of breeding populations for simultaneous QTL mapping, marker validation, marker assisted selection (MAS), and cultivar release has recently caught the attention of plant breeders to circumvent the weaknesses of conventional QTL mapping. The first objective of this study was to test the feasibility of using family-pedigree based QTL mapping techniques generally used with humans and animals within plant breeding populations (PBPs). The second objective was to evaluate two methods (linkage and association) to detect marker-QTL associations. The techniques described in this study were applied to map the well characterized QTL, Fhb1 for Fusarium head blight resistance in wheat (Triticum aestivum L.). The experimental populations consisted of 82 families and 793 individuals. The QTL was mapped using both linkage (variance component and pedigree-wide regression) and association (using quantitative transmission disequilibrium test, QTDT) approaches developed for extended family-pedigrees. Each approach successfully identified the known QTL location with a high probability value. Markers linked to the QTL explained 40-50% of the phenotypic variation. These results show the usefulness of a human genetics approach to detect QTL in PBPs and subsequent use in MAS.

Efficient control of population structure in model organism association mapping

Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.

Potential causes of linkage disequilibrium in a European maize breeding program investigated with computer simulations

Knowledge about the forces generating and conserving linkage disequilibrium (LD) is important for drawing conclusions about the prospects and limitations of association mapping. The objectives of our research were to examine the importance of (1) selection, (2) mutation, and (3) genetic drift for generating LD in a typical maize breeding program. We conducted computer simulations based on genotypic data of Central European maize open-pollinated varieties which have played an important role as founders of the European flint heterotic group. The breeding scheme and the dimensioning underlying our simulations reflect essentially the maize breeding program of the University of Hohenheim. Results suggested that in a plant breeding program of the examined dimension and breeding scheme, genetic drift and selection are major forces generating LD. The currently used population-based association mapping tests do not explicitly correct for LD caused by these two forces. Therefore, increased type I error rates are expected if these tests are applied to plant breeding populations. As a consequence, we recommend to use family-based association tests for association mapping approaches in plant breeding populations.

Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat

Plant breeding data comprise unbalanced phenotypic data for inbreds with complex pedigrees. As traditional methods to map quantitative trait loci (QTL) cannot exploit plant breeding data, an alternative approach is QTL mapping via a mixed-model procedure. Our objective was to validate mixed-model QTL mapping for self-pollinated crops by detecting QTL for kernel hardness and dough strength from data in a bread wheat (Triticum aestivum L.) breeding program. We studied 80 parental and 373 experimental inbreds genotyped for 65 simple sequence repeat (SSR) markers and three candidate loci. The methodology involved three steps: variance component estimation, single-marker analyses, and a final multiple-marker analysis with marker effects treated as fixed effects. Two QTLs for kernel hardness were detected on chromosomes 1A (close to candidate locus GluA3) and 5D (close to candidate locus Ha). Four QTLs were detected for dough strength on chromosomes 1A, 1B, 1D, and 5B. Candidate gene GluA1, which was associated with dough strength, was the only candidate locus found significant. Results were consistent with previously reported markers and QTLs associated with kernel hardness and dough strength. Unlike previous studies that have assumed QTL effects as random, the assumption of fixed marker effects identified the favorable marker alleles to select for. We conclude that the detection of previously mapped QTL validates the usefulness of mixed-model QTL mapping in the context of a plant-breeding program.