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• Awns are stiff, hair-like structures which grow from the lemmas of wheat (Triticum aestivum) and other grasses that contribute to photosynthesis and play a role in seed dispersal. Variation in awn length in domesticated wheat is controlled primarily by three major genes, most commonly the dominant awn suppressor Tipped1 (B1). This study identifies a transcription repressor responsible for awn inhibition at the B1 locus. • Association mapping was combined with analysis in biparental populations to delimit B1 to a distal region of 5AL colocalized with QTL for number of spikelets per spike, kernel weight, kernel length, and test weight. • Fine-mapping located B1 to a region containing only two predicted genes, including C2H2 zinc finger transcriptional repressor TraesCS5A02G542800 upregulated in developing spikes of awnless individuals. Deletions encompassing this candidate gene were present in awned mutants of an awnless wheat. Sequence polymorphisms in the B1 coding region were not observed in diverse wheat germplasm whereas a nearby polymorphism was highly predictive of awn suppression. • Transcriptional repression by B1 is the major determinant of awn suppression in global wheat germplasm. It is associated with increased number of spikelets per spike and decreased kernel size.

Background Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL. Results We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models. Conclusions In a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

Premise An informatics approach was used for the construction of an Axiom genotyping array from heterogeneous, high‐throughput sequence data to assess the complex genome of loblolly pine (Pinus taeda). Methods High‐throughput sequence data, sourced from exome capture and whole genome reduced‐representation approaches from 2698 trees across five sequence populations, were analyzed with the improved genome assembly and annotation for the loblolly pine. A variant detection, filtering, and probe design pipeline was developed to detect true variants across and within populations. From 8.27 million variants, a total of 642,275 were evaluated and 423,695 of those were screened across a range‐wide population. Results The final informatics and screening approach delivered an Axiom array representing 46,439 high‐confidence variants to the forest tree breeding and genetics community. Based on the annotated reference genome, 34% were located in or directly upstream or downstream of genic regions. Discussion The Pita50K array represents a genome‐wide resource developed from sequence data for an economically important conifer, loblolly pine. It uniquely integrates independent projects that assessed trees sampled across the native range. The challenges associated with the large and repetitive genome are addressed in the development of this resource.

Fusiform rust disease, caused by the endemic fungus Cronartium quercuum f. sp. fusiforme, is the most damaging disease affecting economically important pine species in the southeast United States. Unlike the major epidemics of agricultural crops, the co-evolved pine-rust pathosystem is characterized by steady-state dynamics and high levels of genetic diversity within environments. This poses a unique challenge and opportunity for the deployment of large-effect resistance genes. We used trait dissection to study the genetic architecture of disease resistance in two P. taeda parents that showed high resistance across multiple environments. Two mapping populations (full-sib families), each with ~1000 progeny, were challenged with a complex inoculum consisting of 150 pathogen isolates. High-density linkage mapping revealed three major-effect QTL distributed on two linkage groups. All three QTL were validated using a population of 2057 cloned pine genotypes in a 6-year-old multi-environmental field trial. As a complement to the QTL mapping approach, bulked segregant RNAseq analysis revealed a small number of candidate nucleotide binding leucine-rich repeat genes harboring SNP associated with disease resistance. The results of this study show that in P. taeda, a small number of major QTL can provide effective resistance against genetically diverse mixtures of an endemic pathogen. These QTL vary in their impact on disease liability and exhibit additivity in combination.

Genomic prediction has the potential to significantly increase the rate of genetic gain in tree breeding programs. In this study, a clonally replicated population (n = 2063) was used to train a genomic prediction model. The model was validated both within the training population and in a separate population (n = 451). The prediction abilities from random (20% vs 80%) cross validation within the training population were 0.56 and 0.78 for height and stem form, respectively. Removal of all full-sib relatives within the training population resulted in ∼50% reduction in their genomic prediction ability for both traits. The average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form. The degree of genetic linkage (full-sib family, half sib family, unrelated) between the training and validation sets had a strong impact on prediction ability for stem form but not for height. A dominant dwarfing allele, the first to be reported in a conifer species, was discovered via genome-wide association studies on linkage Group 5 that conferred a 0.33-m mean height reduction. However, the QTL was family specific. The rapid decay of linkage disequilibrium, large genome size, and inconsistencies in marker-QTL linkage phase suggest that large, diverse training populations are needed for genomic selection in Pinus taeda L.

Genetic parameters were estimated using a five-series multienvironment trial of Pinus taeda L. in the southern USA. There were 324 half-sib families planted in five test series across 37 locations. A set of six variance/covariance matrices for the genotype-by-environment (G × E) effect for tree height and diameter were compared on the basis of model fit. In single-series analysis, extended factor analytical models provided generally superior model fit to simpler models for both traits; however, in the combined-series analysis, diameter was optimally modeled using simpler variance/covariance structures. A three-way compound term for modeling G × E interactions among and within series yielded substantial improvements in terms of model fit and standard errors of predictions. Heritability of family means ranged between 0.63 and 0.90 for both height and diameter. Average additive genetic correlations among sites were 0.70 and 0.61 for height and diameter, respectively, suggesting the presence of some G × E interaction. Pairs of sites with the lowest additive genetic correlations were located at opposite ends of the latitude range. Latent factor regression revealed a small number of parents with large factor scores that changed ranks significantly between southern and northern environments.

Genotyping platforms for breeding programs should be repeatable, reliable, high-throughput, and cost-effective. In this study, we compared the efficiency of an Affymetrix genotyping array (Pita50K) and a targeted genotyping-by-sequencing (tGBS) panel for estimating genomic relationships and detecting potential pedigree errors in a Pinus taeda L. population. The genomic coordinates of 1000 high-quality single nucleotide polymorphic (SNP) markers from the array were used to design primers for amplicon sequencing via AgriSeq™ tGBS technology. The AgriSeq SNP markers were validated through a comparative study. A sample of 192 Pinus taeda individuals with known pedigree were genotyped with the AgriSeq panel as well as the Pita50K array. The two genotyping datasets were compared on the basis of pedigree errors, the contrast between expected and realized genomic relationships, and the genetic clustering of known families. Realized genomic relationships estimated from the AgriSeq panel and the Pita50K genotyping array had a correlation of 0.90. The median (1.077) of inbreeding coefficients from the AgriSeq panel was considerably higher than the median inbreeding coefficients from the pedigree-based (1.000) and SNP array markers-based estimates (1.001). The proportion of significant discrepancy between realized and expected genetic relationships was about 1% in the population. Principal component analysis of realized genetic relationship matrices from two panels showed similar clustering of full-sib families and revealed pedigree errors in the population. The genotyping cost per sample using the AgriSeq platform for P. taeda was one-third of the cost of using the Pita50K SNP array. The platform is a reliable low-density method for genotyping large numbers of samples. Research is underway to test the AgriSeq markers for Mendelian inconsistency and genomic prediction in P. taeda breeding. Other potential research could be marker-assisted backcrossing, F2 enrichment, or prescribed matings between individuals known to carry large-effect resistance genes.

Conifer breeding is one of the purest applications of quantitative genetics, since there are few qualitative traits distinguishable to the human eye. From the standpoint of a casual observer, a conifer progeny test appears as a homogeneous forest planted at regularly spaced intervals. The subtlety of trait variation and the environmental plasticity of tree growth requires consistent recording of phenotypic measurements across multiple environments. Optimal selection decisions can only be made after quantitative genetic analysis, either through traditional pedigree-based linear mixed models (ABLUP) or through genome-wide regression models (GBLUP). The first chapter is an overview of quantitative genetic methods in conifer improvement, with a focus on the application of genomic markers for reducing the breeding cycle.Second, a multi-environmental trial was analyzed using ABLUP for the purpose of estimating genetic parameters and understanding patterns of genotype-by-environment (GxE) interaction. The trial consisted of 324 maternal half-sib families planted in five test series across 37 locations in the southeast United States. Tree height and diameter were analyzed using multiple variance/covariance matrices for the GxE effect. Models were compared on the basis of model fit. Heritability of family means ranged between 0.63 and 0.90 for both height and diameter. Average additive genetic correlations among sites were 0.70 and 0.61 for height and diameter, respectively, suggesting the presence of some genotype by environment interaction. Pairs of sites with the lowest additive genetic correlations were located at opposite ends of the latitude range.Third, a trait dissection study was conducted to understand the genetic architecture of disease resistance to fusiform rust. Two full-sib families, each with 1000 progeny, were challenged with a complex inoculum consisting of 150 pathogen isolates of Cronartium quercuum f. sp. fusiforme. High-density linkage mapping revealed three major-effect QTL distributed on two linkage groups. All three QTL were validated using a population of 2057 cloned pine genotypes in a six-year-old multi-environmental field trial. As a complement to the QTL mapping approach, bulked segregant RNAseq analysis revealed a small number of candidate genes harboring SNP significantly associated with disease resistance. The results of this study showed for the first time that in P. taeda, a small number of major QTL can provide effective resistance against genetically diverse mixtures of an endemic pathogen. These QTL vary in their impact on disease liability and exhibit additivity in combination.The final chapter is a study of genomic prediction using a clonally propagated training population. In this study, a clonally replicated population (n=2063) was used to train a genomic prediction model. The model was validated both within the training population and in a separate population (n=451). The prediction abilities from random (20% vs 80%) cross validation within the training population were 0.56 and 0.78 for height and stem form, respectively. Removal of all full-sib relatives within the training population resulted in ~50% reduction in their genomic prediction ability for both traits. The average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form. The degree of genetic similarity(full sib family, half sib family, unrelated) between the training and validation sets had a strong impact on prediction ability for stem form but not for height.

Abstract Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL. We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models. In a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵* Gina.Brown-Guedira{at}ars.usda.gov * https://triticeaetoolbox.org/wheat/