Two bread wheat (T. aestivum L.) accessions were selected as parental lines. Population genotyping was conducted on 143 F2 plants and phenotyping was carried out on 133 F2:3 families. The molecular genetic linkage map was constructed including 293 loci associated to 19 wheat chromosomes. There are 76 new loci compared to the ITMI map. The analysis revealed eight QTLs for days to flowering and seven QTLs for plant height. Five QTLs for spike length were identified. The QTL for seed length on chromosome 5B was mapped for all trait measurements under both conditions. The present study revealed four and six QTLs for thousand-grain weight under control and stress conditions, respectively. Only one QTL on chromosome 4BL was common for both conditions. Five QTLs for thousand-grain weight were found to be specific to stress condition on chromosomes 1B, 4AL, 7AS, and 7DS. Identifying QTLs for thousand-grain weight under post-anthesis drought stress on chromosomes 7DS, 7AS, and 4AL and considering the known reciprocal translocation of 4AL/7BS in wheat, revealed the importance of the chromosomes from the homoeologous group 7 of Triticeae.

The SNPs generated through genotyping-by-sequencing (GBS) can be used to develop high-density linkage maps for precision QTL mapping. The present study was undertaken to (1) determine the genetic variability in recombinant inbred line (RIL) population derived from the cross between 'Harry' (drought tolerant) and 'Wesley' (drought susceptible) winter wheat (Triticum aestivum L.), (2) develop high-density linkage map based on GBS derived SNPs, (3) validate the quality of linkage map, (4) and perform the genome-wide QTL mapping of agronomic traits evaluated across multiple rainfed environments. High levels of variation and transgressive segregants were observed among RILs. A high-density linkage map was constructed containing 3,641 markers distributed on 21 chromosomes and spanned 1,959 cM. The map showed strong co-linearity with POPSEQ-based on the high-density linkage map. The accuracy of the linkage map for QTL mapping was confirmed by co-localizing the genomic regions for two highly heritable traits: chaff color and leaf cuticular wax. Genome-wide mapping identified 89 additive effect QTL for all the traits across all environments. Major effect and stable QTL were identified for a flowering date, flag leaf length, flag leaf width, grain yield and plant height. Co-located QTLs were evident for all traits, and one region on chromosome 6B harbored QTL for grain yield and yield component traits. QTL expression was highly variable across the environments, and strong QTL x environment interaction was observed for grain yield, flag leaf area and less so for plant height and thousand kernel weight. Digenetic interactions were notable but highly variable across the environments and explained less phenotypic variance than main effect QTLs. The major QTLs identified may provide a foundation for future studies to fine map and identify key genes underlying these QTLs and to introgress these QTLs to fine tune plant height, adaptation and grain yield under rainfed environments in Great Plains.

This book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture, forestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping, and assumes a background in regression analysis and maximum likelihood approaches. The strength of this book lies in the construction of general models and algorithms for linkage analysis, as well as in QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops.

Wheat is among the top three cereal crops with over ca. 600 million tons being harvested annually. In terms of its range of cultivation no other crop can rival wheat. It was first cultivated over 10,000 years ago as humans shifted from hunting and gathering to settled agriculture. Since then wheat has seen more than a threefold increase in grain yield and makes up ca. 20% of the human diet. Today climate change and increased incidence of drought in areas a wheat production negatively impact grain yield. This has prompted interest in studying root system traits and how those traits may improve drought tolerance. For these reasons, the research in this dissertation was aimed at identifying quantitative trait loci (QTLs) and allelic variation for root system traits while also gaining an understanding of root and shoot relationships. To accomplish this three integrated mapping populations of bread wheat were created and sets of unique experiments were conducted. Significant variation for root system traits was observed in all three populations and QTLs were identified and verified for some of these traits. One major QTL for seminal root angle on chromosome arm 2DS was verified in two of the three mapping populations. Additionally, we were able to draw some general conclusions about the relationship between root and shoot biomass within the materials we tested. Using over ca. 6,000 data points we observed that as root biomass continues to increase beyond a certain threshold it negatively impacts grain yields and shoot biomass. However, in individual cultivars this relationship may be entirely different, with root biomass increasing proportionately to increasing grain yields without any observable threshold. When testing for allelic variation at a locus thought to control root biomass on rye chromosome arm 1RS we were unable to identify any significant differences between sources of the 1RS translocation. In a similar study testing for allelic variation for a locus on wheat chromosome arm 1BS thought to control root system plasticity in response to drought we were also unable to identify any significant difference between 1B substitution lines in a common genetic background of cv. Pavon 76.

In Quantitative Trait Loci: Methods and Protocols, a panel of highly experienced statistical geneticists demonstrate in a step-by-step fashion how to successfully analyze quantitative trait data using a variety of methods and software for the detection and fine mapping of quantitative trait loci (QTL). Writing for the nonmathematician, these experts guide the investigator from the design stage of a project onwards, providing detailed explanations of how best to proceed with each specific analysis, to find and use appropriate software, and to interpret results. Worked examples, citations to key papers, and variations in method ease the way to understanding and successful studies. Among the cutting-edge techniques presented are QTDT methods, variance components methods, and the Markov Chain Monte Carlo method for joint linkage and segregation analysis.