Genome-wide Association Study for Beta-glucan Concentration in Elite North American Oat

Genome-wide association studies (GWAS) can be a useful approach to detect quantitative trait loci (QTL) controlling complex traits in crop plants. Oat (Avena sativa L.) β-glucan is a soluble dietary fiber and has been shown to have positive health benefits. We report a GWAS involving 446 elite oat breeding lines from North America genotyped with 1005 diversity arrays technology (DArT) markers and with phenotypic data from both historical and balanced 2-yr data. Association analyses accounting for pair-wise relationships and population structure were conducted using single-marker tests and least absolute shrinkage and selection operator (LASSO). Single-marker tests yielded six and 15 significant markers for the historical and balanced data sets, respectively. The LASSO method selected 24 and 37 markers as the most important in explaining β-glucan concentration for the historical and balanced data sets, respectively. Comparisons of genetic location showed that 15 of the markers in our study were found on the same linkage groups as QTL identified in previous studies. Four of the markers colocalized to within 4 cM of three previously detected QTL, suggesting concordance between QTL detected in our study and previous studies. Two of the significant markers were also adjacent to a β-glucan candidate gene in the rice (Oryza sativa L.) genome. Our findings suggest that GWAS can be used for QTL detection for the purpose of gene discovery and for marker-assisted selection to improve β-glucan concentration in elite oat

A candidate-gene association study for berry colour and anthocyanin content in Vitis vinifera L.

Anthocyanin content is a trait of major interest in Vitis vinifera L. These compounds affect grape and wine quality, and have beneficial effects on human health. A candidate-gene approach was used to identify genetic variants associated with anthocyanin content in grape berries. A total of 445 polymorphisms were identified in 5 genes encoding transcription factors and 10 genes involved in either the biosynthetic pathway or transport of anthocyanins. A total of 124 SNPs were selected to examine association with a wide range of phenotypes based on RP-HPLC analysis and visual characterization. The phenotypes were total skin anthocyanin (TSA) concentration but also specific types of anthocyanins and relative abundance. The visual assessment was based on OIV (Organisation Internationale de la Vigne et du Vin) descriptors for berry and skin colour. The genes encoding the transcription factors MYB11, MYBCC and MYC(B) were significantly associated with TSA concentration. UFGT and MRP were associated with several different types of anthocyanins. Skin and pulp colour were associated with nine genes (MYB11, MYBCC, MYC(B), UFGT, MRP, DFR, LDOX, CHI and GST). Pulp colour was associated with a similar group of 11 genes (MYB11, MYBCC, MYC(B), MYC(A), UFGT, MRP, GST, DFR, LDOX, CHI and CHS(A)). Statistical interactions were observed between SNPs within the transcription factors MYB11, MYBCC and MYC(B). SNPs within LDOX interacted with MYB11 and MYC(B), while SNPs within CHI interacted with MYB11 only. Together, these findings suggest the involvement of these genes in anthocyanin content and on the regulation of anthocyanin biosynthesis. This work forms a benchmark for replication and functional studies

Genomic, Marker-Assisted, and Pedigree-BLUP Selection Methods for β-Glucan Concentration in Elite Oat

β-glucan, a soluble fiber found in oat (Avena sativa L.) grain, is good for human health, and selection for higher levels of this compound is regarded as an important breeding objective. Recent advances in oat DNA markers present an opportunity to investigate new selection methods for polygenic traits such as β-glucan concentration. Our objectives in this study were to compare genomic, marker-assisted, and best linear unbiased prediction (BLUP)–based phenotypic selection for short-term response to selection and ability to maintain genetic variance for β-glucan concentration. Starting with a collection of 446 elite oat lines from North America, each method was conducted for two cycles. The average β-glucan concentration increased from 4.57 g/100 g in Cycle 0 to between 6.66 and 6.88 g/100 g over the two cycles. The averages of marker-based selection methods in Cycle 2 were greater than those of phenotypic selection (P \u3c 0.08). Progenies with the highest β-glucan came from the marker-based selection methods. Marker-assisted selection (MAS) for higher β-glucan concentration resulted in a later heading date. We also found that marker-based selection methods maintained greater genetic variance than did BLUP phenotypic selection, potentially enabling greater future selection gains. Overall, the results of these experiments suggest that genomic selection is a superior method for selecting a polygenic complex trait like β-glucan concentration

Genome-wide Association Analysis Tracks Bacterial Leaf Blight Resistance Loci In Rice Diverse Germplasm

Genome-wide association analysis of bacterial blight resistance to nine Xoo strains in 198 indica genotypes based on Efficient Mixed-Model Association eXpedited Model (EMMAX). Manhattan plots for nine Xoo strains (a) PXO61, (b) PXO86, (c) PXO79, (d) PXO71, (e) PXO112, (f) PXO99, (g) PXO339, (h) PXO349, and (i) PXO341. X-axis shows the SNPs along each chromosome; y axis is the − log10 (P-value) for the association. Significant SNPs are those beyond the red line having P-value < 1 × 10 −5. Quantile-quantile plots for nine Xoo strains (j) PXO61, (k) PXO86, (l) PXO79, (m) PXO71, (n) PXO112, (o) PXO99, (p) PXO339, (q) PXO349, and (r) PXO341. (PPTX 521 kb

STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS

Genome-wide association studies (GWAS) have successfully detected tens of thousands of robust SNP-trait associations. Earlier researches have primarily focused on association studies of genetic variants and some well-defined functions or phenotypic traits. Emerging evidence suggests that pleiotropy, the phenomenon of one genetic variant affects multiple phenotypes, is widespread, especially in complex human diseases. Therefore, individual phenotype analyses may lose statistical power to identify the underlying genetic mechanism. Contrasting with single phenotype analyses, joint analysis of multiple phenotypes exploits the correlations between phenotypes and aggregates multiple weak marginal effects and is therefore likely to provide new insights into the functional consequences of genetic variations. This dissertation includes two papers, corresponding to two primary research projects I have done during my Ph.D. study, with each distributed in one chapter. Chapter 1 proposed an innovative method, which referred to as HC-CLC, for joint analysis of multipole phenotypes using a Hierarchical Clustering (HC) approach followed by a Clustering Linear Combination (CLC) method. The HC step partitions phenotypes into clusters. The CLC method is then used to test the association between the genetic variant and all phenotypes, which is done by combining individual test statistics while taking full advantage of the clustering information in the HC step. Extensive simulations together with the COPDGene data analysis have been used to assess the Type I error rates and the power of our proposed method. Our simulation results demonstrate that the Type I error rates of HC-CLC are effectively controlled in different realistic settings. HC-CLC either outperforms all other methods or has statistical power that is very close to the most powerful alternative method with which it has been compared. In addition, our real data analysis shows that HC-CLC is an appropriate method for GWAS. Chapter 2 redesigned the PheCLC (Phenome-wide association study that uses the CLC method) which was previously developed by our research group. The refined method is then applied on the UKBiobank data, a large cohort study across the United Kingdom, to test the validity and understand the limitations of the proposed method. We have named our new method UKB-PheCLC. The UKB-PheCLC method is an EHR-based PheWAS. In the first step, it classifies the whole phenome into different phenotypic categories according to the UK Biobank ICD codes. In the second step, the CLC method is applied to each phenotypic category to derive a CLC-based p-value for testing the association between the genetic variant of interest and all phenotypes in that category. In the third step, the CLC-based p-values of all categories are combined by using a strategy resemble that of the Adaptive Fisher\u27s Combination (AFC) method. Overall, UKB-PheCLC harnesses the powerful resource of the UK Biobank and considers the possibility that phenotypes can be grouped into different phenotypic categories, which is very common in EHR-based PheWAS. Moreover, UKB-PheCLC can handle both qualitative and quantitative phenotypes, and it also doesn’t require raw phenotype information. The real data analysis results confirm that UKB-PheCLC is more powerful than the existing methods we have it compared with. Thus, UKB-PheCLC can serve as a compelling method for phenome-wide association study

Breeding for β-glucan content in elite North American oat (Avena sativa L.) using molecular markers

This dissertation explored genomewide association study (GWAS) and conducted actual breeding program in oat using different selection methods to identify molecular markers and improve beta-glucan content- a trait with positive health benefits. Results from GWAS suggested that beta-glucan content in elite oat is controlled by many QTL with small effects. Some of the important markers in our study co-localized with QTL in previous linkage studies. For the selection study, results demonstrated that after two cycles of selection the population means from marker-assisted selection and genomic selection methods were higher than BLUP phenotypic selection. The study also showed that the top performing lines came from marker-based methods indicating superiority of these methods in terms of cultivar development. The top lines in this study were also submitted to National Small Grains Collection for germplasm preservation and distribution purposes. We also found that the genetic variance for beta-glucan is mainly controlled by additive genetic component. However, the genetic variance decreased after two cycles of selection but the magnitude of decrease was different between selection methods. Particularly, the greatest reduction in genetic variance was detected for populations undergoing BLUP phenotypic selection. This could be attributed to higher chance of co-selection of sibs. On the other hand, populations under genomic selection had the lowest reduction in genetic variance which could be attributed to ability of markers to detect segregation in the estimation of breeding values. Among the three methods, only genomic selection can be conducted atleast twice a year which can result to doubling of genetic gain. Our experiments also demonstrated empirically that the accuracy of genomic selection can be increased by larger training population size, higher marker density and if selection candidates are genetically related to the training population

Genetic and genomic analysis of polyploid Chrysanthemum hybrids with emphasis on shoot branching

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Characterization of a diverse USDA collection of wild soybean (glycine soja siebold & zucc.) accessions and subsequent mapping for seed composition and agronomic traits in a RIL population

Dr. Andrew Scaboo, Dissertation Supervisor.Includes vita.Field of study: Plant insect and microbial sciences."July 2018."The relatively low genomic variation of current U.S. soybean [Glycine max (L.) Merill] cultivars constrains the improvement of grain yield, seed quality, and other agronomic traits within soybean breeding programs. Recently, a substantial effort has been undertaken to introduce novel genetic diversity present in wild soybean (Glycine soja Siebold & Zucc.) into new elite cultivars, in both public and private applied soybean breeding programs. The objectives of this research were to evaluate the phenotypic diversity within a core collection of 80 G. soja plant introductions (PIs) in the United States Department of Agriculture National Genetic Resources Program that were collected in China, Japan, Russia, and South Korea, and to analyze the correlations between agronomic and seed composition traits. Field tests were conducted in Missouri and North Carolina during three years, 2013, 2014, and 2015, in a randomized complete block design (n=3). The phenotypic data collected included plant maturity date, seed weight, and the seed concentration of protein, oil, essential amino acid, fatty acid, and soluble carbohydrates. Analyzing the data from six environments, we found genotype was a significant (p < 0.0001) source of variation for maturity date, seed weight, seed protein and amino acids, seed oil and fatty acids, and seed carbohydrates. Significant correlations were observed between numerous traits. The core collection had lower seed weight, higher seed content of protein, linolenic acid, raffinose and stachyose but lower seed content of oil and oleic acid than those of the cultivated soybean lines that were used as checks. The amino acid profile of the core collection was significantly different from that of the checks. An association analysis revealed 19 SNP that were significantly associated with maturity, seed weight, and seed contents of aspartic acid, glutamine, palmitic acid, oleic acid, and linoleic acid. The information and data collected in this study will be invaluable in guiding soybean breeders and geneticists in selecting promising Glycine soja plant introductions for research and cultivar improvement. In addition the identification of quantitative trait loci (QTLs) associated with the contents of seed protein and oil, maturity, branching traits, height, lodging, and yield in a recombinant inbred line (RIL) population developed from one single F2 plant from the cross between Osage and PI593983 was carried out. The mapping population in this study included 164 F4:6 recombinant inbred lines (RILs) derived from a cross between Osage, a cultivated soybean variety, and PI593983, a wild soybean accession. Field tests were carried out in Missouri for two years during 2016 and 2017, in a randomized complete block design (n=2). Both protein and oil contents showed high heritabilities. Seed protein and seed oil were negatively correlated (-0.77). A total of 4,374 polymorphic markers were used to construct a genetic linkage map, and nine QTLs for protein content, explained 7.6 to 36.7% of variance, and seven QTLs for oil content, explained for 7.8 to 29.7% of variance, were detected using composite interval mapping. addition we identified eight novel QTLs and confirmed sixteen QTLs associated with maturity (R2 = 6.4 to 26.3%), plant height (R2 = 7.4 to 15.5%), and total branch length (R2 = 9.3% and 14.5%) in individual and across environments, and the ratio of total branch length to plant height (R2 = 11.8%), yield (R2 =12.8 and 15.7), and lodging (R2 = 12.1 and 13.4) in individual studied environments. Sixteen QTLs for maturity, yield, and plant height confirmed previously reported QTLs, and eight QTLs have not been reported before. The results of this study will facilitate the identification of the causative genes for seed protein and oil, maturity, height, lodging, and branching traits, and will help soybean breeder improve soybean performance by developing markers for marker-assisted selection.Includes bibliographical references