Assessing the impact of genotype calling methodologies on genome wide association studies in polyploids and evaluating key yield traits of Miscanthus sacchariflorus

Njuguna, Joyce Njoki

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https://hdl.handle.net/2142/120486 Copy

Description

Title

Assessing the impact of genotype calling methodologies on genome wide association studies in polyploids and evaluating key yield traits of Miscanthus sacchariflorus

Author(s)

Njuguna, Joyce Njoki

Issue Date

2023-01-26

Director of Research (if dissertation) or Advisor (if thesis)

Clark, Lindsay V

Doctoral Committee Chair(s)

Sacks, Erik J

Committee Member(s)

• Lipka, Alexandar E
• Rutkoski, Jessica E
• Ming, Ray

Department of Study

Crop Sciences

Discipline

Crop Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Biomass yield
• Miscanthus sacchariflorus
• genetic diversity
• Miscanthus ×giganteus
• bioenergy
• multi-location field trials
• genomic prediction
• allelic dosage
• polyploids
• genotype calling
• genome wide association study

Abstract

Discovery and analysis of the genetic variants underlying agriculturally important traits is key to molecular breeding and improvement of polyploid crops. Reduced representation approaches have provided fast and cost-efficient genotyping using next-generation sequencing (NGS). However, accurate genotype-calling from NGS data is challenging, particularly in polyploid species where there is uncertainty in allelic dosage due to the genome multiplicity, complex inheritance patterns and low sequencing depth. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG and updog, account for uncertainty by incorporating error rates and population parameters to accurately estimate allelic dosage across any ploidy. In this study, we evaluated these three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using field-trial and marker data from two autotetraploid species, Miscanthus sacchariflorus and southern highbush blueberry, and performed GWAS and genomic prediction. These experiments showed that the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that polyRAD outperformed the other methods in its effect on GWAS power and by limiting false positives. In addition, we found that continuous genotypes increased the accuracy of genomic prediction, particularly at low sequencing depths. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops. Miscanthus is a high-yielding bioenergy crop that is broadly adapted to temperate and tropical environments. Commercial cultivation of Miscanthus is predominantly limited to a single sterile triploid clone of Miscanthus ×giganteus, a hybrid between Miscanthus sacchariflorus and Miscanthus sinensis. To expand the genetic base of M. ×giganteus, the substantial diversity within its progenitor species should be used for cultivar improvement and diversification. Here we phenotyped a diversity panel of 605 M. sacchariflorus and 27 M. ×giganteus genotypes for dry biomass yield and 16 yield-component traits, in field trials grown over three years at one subtropical location (Zhuji, China) and four temperate locations (Foulum, Denmark; Sapporo, Japan; Urbana, Illinois; and Chuncheon, South Korea). There was considerable diversity in yield and yield-component traits among and within genetic groups of M. sacchariflorus, and across the five locations. Biomass yield of M. sacchariflorus ranged from 0.003-34.0 Mg ha-1 in year 3. Variation among the genetic groups was typically greater than within, so selection of genetic group should be an important first step for breeding with M. sacchariflorus. The Yangtze 2x genetic group (=ssp. lutarioriparius) of M. sacchariflorus had the tallest and thickest culms at all locations tested. Notably, the Yangtze 2x genetic group’s exceptional culm length and yield-potential was driven primarily by a large number of nodes (>29 nodes culm-1 average over all locations), which was consistent with the especially late flowering of this group. The S Japan 4x, the N China/Korea/Russia 4x and the N China 2x genetic groups were also promising genetic resources for biomass yield, culm length and culm thickness, especially for temperate environments. Culm length was the best indicator of yield potential in M. sacchariflorus. These results will inform breeders’ selection of M. sacchariflorus genotypes for population improvement and adaptation to target production environments. Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic-enabled breeding tools, like marker assisted selection (MAS) and genomic selection (GS), has the potential to improve the efficiency of Miscanthus breeding. We conducted genome-wide association (GWA) analyses and genomic prediction for biomass yield and 14 yield-components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across four years in one subtropical and three temperate locations and genotyped with 268,109 single nucleotide polymorphisms (SNPs). The GWAS study identified a total of 835 significant SNPs and 682 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in previously mapped QTL intervals or proximal to SNPs identified for similar traits in other Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield-component traits in M. sacchariflorus that may facilitate marker assisted breeding for biomass yield. Genomic prediction accuracy for the yield related traits ranged from 0.15-0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N=237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26-0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus. This study indicates that MAS and genomic prediction will likely be beneficial for conducting population-improvement of M. sacchariflorus.

Graduation Semester

2023-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Joyce Njuguna

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