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An understanding of the genetic architecture of complex traits - the location of genes in the genome, distribution of effect sizes, and preponderance of major genes - can help determine the most appropriate breeding strategy. We use genetic mapping, including linkage mapping and association mapping, to improve this understanding in traits such as flowering time, yield, barley malting quality, and disease resistance.
In linkage mapping, we develop populations of recombinants from parents with divergent phenotypes and use molecular markers to determine the location and effect of major genes. By contrast, association mapping requires no population development, only a diverse panel of genotypes with variation for one or many traits. Molecular markers are still used to identify genes, but we can obtain finer estimates of gene location through association mapping. We have conducted research in a hybrid of both approaches, nested association mapping, where diverse parents are crossed to a single common parent and many populations are developed. In all cases, validated molecular markers that are linked to major genes can be used in marker-assisted selection to screen plants that have inherited the favorable allele.
Research in plant breeding, evolution, and population genetics over the past few decades has revealed the true extent of polygenicity in complex traits; these traits are influenced not by a handful of major genes but by hundreds or thousands of small-effect genes that are spread diffusely throughout the genome. Improving these traits can take advantage of abundant and inexpensive molecular markers, which provide an estimate of the whole-genome contribution to phenotypes. We are researching ways in which this approach, genomewide prediction, can improve the efficiency of breeding.
In genomewide prediction, a training population with trait and molecular marker data is used to build a statistical model, which is then applied to predict the trait phenotypes in a test population. Plants in the test population can be screened on the basis of these predictions, instead of, or in addition to, field phenotypic measurements. In the breeding program, we are used genomewide prediction to predict yield, disease resistance, barley malting quality, and other traits that are expensive or laborious to phenotype. We are also exploring additional uses of genomewide prediction, including the prediction of genetic (co)variance in crosses and genotype-environment interactions, and alternative experimental designs for more efficient resource allocation.
Measure what is measurable, and make measurable what is not so. - Galileo Galilei
Collecting phenotypic data on plants in the field or in the greenhouse will always be critical for breeding; however, there are many obstacles to obtaining high-quality phenotypic data, such as spatial variability in a field, temporal variability in a growing season, and technological limitations.
High throughput phenotyping
Our lab is conducting research to develop more effective experimental designs to improve data precision, use unmanned aircraft systems to collect data quickly at many intervals in the growing season, and design innovative approaches to measure traits that have historically been more challenging.