ABI Innovation: Improved modeling of marker-trait associations in polypoid and diploid organisms using genotyping-by-sequencing with genotype uncertainty

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

Many economically important crop plants have more than two copies of each chromosome - they have more than 2 alleles of each gene in their genome and the genotypes for their important traits should reflect the level of polyploidy. Genotypes are used to predict the phenotype in breeding programs, for traits like flowering time, seed weight or oil yield. Marker genotypes for such traits are currently determined in targeted sequencing experiments, termed genotype-by-sequencing (GBS). GBS has drastically reduced the cost of detecting genetic variants and determining the genotypes of individuals, thus making high-throughput genotyping more accessible within the fields of agriculture and ecology. However, marker genotypes determined using GBS are frequently incorrect because, due to random chance, some alleles do not get sequenced in some individuals. Polyploidy further complicates the issue of genotype ambiguity because genotypes can no longer simply be classified as homozygous or heterozygous, but instead are defined by the number of copies of each allele that they possess. This project aims to develop methodology and software for quantifying uncertainty in DNA marker genotypes determined by genotyping-by-sequencing (GBS), particularly in polyploid species, and for incorporating that uncertainty into analyses that relate genotype to phenotype. Given that many economically-important crops are polyploid, genotyping improvements that result from this project will be especially beneficial for securing the world's supply of food, fuel, and fiber through marker-assisted plant breeding. This project will result in increased sensitivity for identifying marker alleles that are associated with phenotypic differences, as well as improved prediction of phenotypes from GBS data. More broadly, it will promote a paradigm shift in the field, treating genotypes as probability distributions rather than values that are known with certainty. Additionally, YouTube videos teaching linear algebra as well as computer programming in Python and R will be created during the course of this project, making these concepts more accessible for students in the biological sciences. The objectives of this project are to (1) create an algorithm that estimates genotype probabilities in diploids and polyploids as accurately as possible from GBS data and (2) create genome-wide association study (GWAS) and genomic selection (GS) methodologies that fully utilize genotype probabilities. An iterative algorithm will be developed that will generate a probability distribution of each individual being each possible genotype at a given locus. The algorithm will use read depth at each of two or more alleles and will model multiple biological and technical parameters, including allele frequencies, population structure, inbreeding, linkage disequilibrium, inheritance mode, differential amplification, and sample contamination. The algorithm will be implemented in a publicly-available R package that will integrate with existing GBS bioinformatics pipelines and will output multiple formats suitable for downstream analysis. New GWAS and GS methods will be developed that model both additive and dominance effects while utilizing genotype probability distributions, and will build upon existing software for GWAS and GS. Diploid and tetraploid populations of the bioenergy grass Miscanthus will be used for validating and improving the new methodologies. GBS and phenotyping have already been performed or are underway on these populations. Software produced as a result of this project will be hosted at https://github.com/lvclark/polyRAD and https://github.com/lvclark/GAPITdom .