Genomic Selection

2024

Training Presentations

User Presentations

Poster Presentations

MagicImpute: Imputation and Error Detection for Sequence Data In Connected Multiparental Populations

Chaozhi Zheng1, Eligio Bossolini2, Antje Rohde2, Martin Boer1, Fred van Eeuwijk1. 1Wageningen University and Research. 2BASF Innovation Center Gent. 

Multiparental populations have been produced for quantitative trait loci (QTL) mapping in many crops, where next-generation sequencing has become a cost-effective tool for genotyping. Previously, we have developed a hidden Markov framework MagicImpute (Zheng et al. 2018) for genotype imputation in a multiparental population, however, its computational time increases quickly with the number of parents. In this work, we extend MagicImpute with increasing computational efficiency and robustness to various types of errors. Particularly, it has the following novel features: (1) allow for multiple multiparental populations that may be connected by sharing parents, (2) allow for many missing parents that are not available for sequencing, (3) account for possible allelic bias and overdispersion in the sequence data model, and (4) infer marker-specific error rates and filter for high quality markers. It currently works for diploids and is extendable to polyploids. Beside extensive simulation studies, we evaluate MagicImpute by three real datasets: the rice F2 population (Furuta et al. 2017) with sequence coverage being low, the apple F1 population (Gardner et al. 2014) with parents being outbred, and the sorghum multi-parent advanced generation intercross (MAGIC) population (Ongom and Ejeta 2018) with 10 male sterile lines (out of 29 parents) being completely missing.

 

Understanding the Genetics of the Flower Color Transition Phenotype in Autotetraploid Roses

Haramrit Gill, Jeekin Lau, Shuyang Zhen, David Byrne, Oscar Riera-Lizarazu. Texas A&M University, College Station, TX.

Flower color is one of the most promising traits in ornamental plants and plays a significant role in roses' aesthetic and economic value. The rose industry thrives on producing novel flower colors and pigmentation patterns. Thus, it is important to understand flower pigmentation patterns. We observed an interesting phenotype that we call ‘flower color transition’ in two tetraploid garden rose populations [Stormy Weather (SW) X Brite Eyes (BE) and My Girl (MG) X Brite Eyes (BE)]. The roses exhibiting this phenotype have flowers that transition from a light yellow to a dark pink/red color as the flower ages, leading to bushes peppered with multiple colors. Here, we present studies to identify the type of pigment accumulated and the candidate genes controlling the accumulation of these pigments by determining quantitative trait loci (QTL) regions, and transcriptomic analysis. The main objective of this research was to gain a better understanding of the genetics and biochemistry of this trait. In the plants that show the ‘flower color transition’ trait, pigment analysis shows a gradual increase in anthocyanin accumulation over different stages of development. In contrast, stably pigmented, non-transitioning roses show either constant, elevated levels of anthocyanins or constant low levels throughout all stages of flower development. In addition, light treatments showed that the ‘flower color transition’ trait is induced by UV-B radiation. Thus, the ‘flower color transition’ trait is UV-B sensitive. The QTL analysis suggests the presence of QTL on linkage groups (LGs) 2, 3, 4, and 6 in the SWXBE population and on LGs 6 and 7 in the MGXBE population. The locations of QTL coincided with the locations of genes involved in the flavonoid biosynthetic pathway. Further, the transcriptomic analysis revealed that the ABC transporters, RhGT1, 3GT, MYB1/PAP1, and SPL9 are some of the main genes involved in the flower color transition phenomenon.

 

Mapping high specific gravity trait in potato using Genotyping by sequencing.

Nima Samadi, Vidyasagar Sathuvalli Rajakalyan, Solomon Yima. Oregon State University, Corvallis, OR. 

Potato varieties that combine high yields and biotic and abiotic stress tolerance with a high percentage of solids are needed by the potato processing industry. The dry matter content of tubers, as measured by specific gravity, is an important measure of quality used by processors to assess the suitability of potatoes for the production of French fries, chips, and dehydrated products. There are many factors that affect the specific gravity of tubers: variety, growing season, planting time, seed quality, planting density, nutrition, irrigation, pests, diseases, and soil type. However, the selection of suitable varieties is the most efficient way to produce potato crops with desired tuber specific gravity. In this study, we aim to identify the genomic regions associated with high specific gravity from a diploid hybrid population of Solanum phureja – S. stenotomum comprising 150 diploid individuals. The population was evaluated for its specific gravity for two years (2018 and 2019) across three distinct locations in Oregon. We employed Genotyping by Sequencing (GBS) to genotype the population, resulting in a total of 47,232 SNPs. Following data imputation and filtering, we identified 3,698 high-quality SNPs for use in the Genome-Wide Association Study and QTL mapping analyses. GWAS and QTL mapping analyses are currently underway. The genomic regions identified from this study will be used to identify candidate genes and to develop molecular markers for marker-assisted breeding for high specific gravity trait.

2023

Training Presentations

User Presentations

Poster presentations

Integration of Genetic and Data-Driven Methods for Optimizing Genomic Prediction in Autotetraploid Blueberries

Paul Adunola, Felipe Ferrao, Patricio Munoz. University of Florida, Gainesville, FL. 

Genomic selection (GS) is a form of marker-assisted selection that estimates the effects of genome-wide markers to predict the genetic merit of non-phenotyped individuals. While its practical implementation required the allocation of resources, breeders must face the choice of budget allocation associated with genotyping and population designs. In this context, genomic prediction applied to blueberry (Vaccinum spp) has experienced extra obstacles, due to the autopolyploid form and perennial nature of the species, a fact that results in slower progress. Inspired by recent improvements in using genomic prediction at the University of Florida Blueberry Breeding Program, we proposed a combination of data-driven and genetic-based methodologies to allocate limited resources on genotyping. To this end, we used ten criteria to select the best probes for sequencing and compared predictive abilities computed using genomic BLUP (GBLUP) and single-step BLUP (ssBLUP). Our contribution to this study is two-fold: (i) first, we emphasize the importance of combining filtering parameters based on statistical genetics features to select the best set of molecular markers and tested genomic prediction studies; (ii) for practical implementation, we showed that number of markers size can be optimized, a fact that can leverage our predictive accuracies with reduced costs. In this study, we demonstrated the effectiveness of filtering criteria to select a panel of probes using data from fruit quality traits collected over the past two years at the University of Florida.

 

Training presentations

2022

2022

Poster presentations

Genomic prediction for yield and processing traits in the tetraploid potato

Jeewan Pandey1, Douglas C. Scheuring1, Jeffrey W. Koym2, and Maria Isabel Vales1. 1Department of Horticultural Sciences, Texas A&M University, College Station, TX. 2Texas A&M AgriLife Research and Extension Center, Lubbock, TX. 

                  The current breeding process to develop a new potato cultivar takes a long time (10–15 years). One way to speed up the process and make it more efficient is to shorten the recurrent selection breeding cycle. This can be achieved by assigning breeding values to clones in the breeding pipeline and bringing those with the most favorable breeding values as parents for the crossing block. The aim of this study was to implement one angle of genomic selection by obtaining breeding values of chipping potato clones and recommend parents for the breeding program. Five hundred and forty-nine unique chipping potato clones were evaluated between 2017 and 2020 near Dalhart, TX, and genotyped using the Illumina Infinium Potato SNP array. Genomic-estimated breeding values (GEBVs) for chip color, chip quality, specific gravity, and total yield were obtained using the package StageWise. Potato clones with the most favorable GEBVs were identified and recommended as parents. The mean reliability of the GEBVs obtained were 0.75, 0.43, 0.61, and 0.33 for chip color, chip quality, specific gravity, and total yield, respectively. Breeders will increase the probability of transferring useful traits from parents to their progeny by choosing parental lines with the most favorable GEBVs. In turn, progeny with the best GEBVs can be re-used as parents or advanced to become new varieties. Thanks to the development of software packages suitable for polyploid species, genomic selection in potatoes is becoming more feasible and attractive.

 

Predicting Length to Width Ratio, Roundness, and Compactness in Potatoes

Michael Miller and Laura Shannon. University of Minnesota, Saint Paul, MN.

                  Potato is an important crop to the global food system; however, the adoption of new potato varieties has been slow compared to many other staple food crops. This is in part due to the importance of many traits beyond yield, in particular quality traits, to the marketability of potatoes. Quality traits are often measured using subjective and imprecise visual scales. These scales introduce error due to rater fatigue, rater experience, and differences in scale interpretation between raters. Visual scales also limit differentiation between potato clones which express a trait at similar but not identical levels. We have developed an image analysis platform in the R programming language to provide objective and precise numeric measurements of several potato tuber quality traits. Among these traits are measurements of tuber shape including length to width ratio, roundness, and compactness. Combining the phenotype data provided by this platform with the genomic selection tools provided by Tools for Polyploids could allow for robust genomic selection models of potato tuber shape quality traits. A collection of 82 chip market class potato clones from the University of Minnesota breeding program were evaluated for shape traits over 3 field seasons. Genotyping was performed using the SolCAP SNP array. Allele dosage calling was performed using the fitPoly R package. The StageWise package was then used to create predictions of genomic estimated breeding values for tuber shape traits.

 

Increasing the Prediction Accuracy in Genomic Selection of Complex Traits using WGBLUP

Cesar A. Medina1, Harpreet Kaur2, Ian Ray2, and Long-Xi Yu1. 1United States Department of Agriculture-Agricultural Research Service, Plant Germplasm, Introduction and Testing Research, Prosser, WA. 2Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM. 

                  Genomic selection (GS) is a variant of marker-assisted selection, in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population to a specific trait. GS is useful in complex traits controlled by many genes with small effects. However, some complex traits such as biomass yield or abiotic stress tolerance have low prediction accuracy (measured as Pearson correlation between GEBV and phenotypic values). There is a need to increase the prediction accuracy to employ GS in breeding programs. In this work, we developed and tested an alternative GS model named weighted GBLUP (WGBLUP). We integrated DNA marker significant values of genome-wide association studies (GWAS) in WGBLUP analyses. We performed a case study using phenotypic data on biomass yield under salt stress of alfalfa and 13 phenotypic traits of potato to validate the WGBLUP model. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress and up to 90% in potato tuber length. The use of the WGBLUP model will allow to implement GS in different breeding programs, increasing the selection accuracy in complex traits.