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

许多经济上重要的作物植物具有多于两个拷贝的每条染色体-它们在其基因组中具有多于2个等位基因的每个基因，并且其重要性状的基因型应反映多倍性水平。基因型被用来预测育种计划中的表型，如开花时间，种子重量或油产量等性状。这些性状的标记基因型目前在靶向测序实验中确定，称为基因型测序（GBS）。GBS大大降低了检测遗传变异和确定个体基因型的成本，从而使高通量基因分型在农业和生态学领域更容易获得。 然而，使用GBS确定的标记基因型经常是不正确的，因为由于随机的机会，一些等位基因在一些个体中没有被测序。 多倍性进一步使基因型模糊性的问题复杂化，因为基因型不能再简单地被分类为纯合或杂合，而是由它们拥有的每个等位基因的拷贝数来定义。 该项目旨在开发方法和软件，用于量化通过测序基因分型（GBS）确定的DNA标记基因型的不确定性，特别是在多倍体物种中，并将该不确定性纳入与基因型表型相关的分析中。鉴于许多具有重要经济意义的作物都是多倍体，该项目所带来的基因分型改进将特别有利于通过标记辅助植物育种来确保世界粮食、燃料和纤维的供应。 该项目将提高识别与表型差异相关的标记等位基因的灵敏度，并改善GBS数据对表型的预测。 更广泛地说，它将促进该领域的范式转变，将基因型视为概率分布，而不是确定性已知的值。 此外，YouTube视频教学线性代数以及计算机编程的Python和R将在这个项目的过程中创建，使这些概念更容易为学生在生物科学。这个项目的目标是（1）创建一个算法，估计基因型概率在二倍体和多倍体尽可能准确地从GBS数据和（2）创建全基因组关联研究（GWAS）和基因组选择（GS）方法，充分利用基因型概率。 将开发一种迭代算法，该算法将生成每个个体是给定基因座处的每个可能基因型的概率分布。 该算法将使用两个或更多个等位基因中的每一个的读取深度，并将对多个生物和技术参数进行建模，包括等位基因频率、群体结构、近交、连锁不平衡、遗传模式、差异扩增和样品污染。 该算法将在一个公开的R包中实现，该包将与现有的GBS生物信息学管道集成，并将输出适合下游分析的多种格式。 将开发新的GWAS和GS方法，在利用基因型概率分布的同时对加性和显性效应进行建模，并将建立在现有的GWAS和GS软件基础上。 生物能源草芒草的二倍体和四倍体群体将用于验证和改进新方法。 GBS和表型分析已经或正在对这些人群进行。 该项目产生的软件将在https://github.com/lvclark/polyRAD和https://github.com/lvclark/GAPITdom上托管。

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 .