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Improved capacity of genomics and biotechnology has greatly enhanced genetic studies in different areas. Genomic selection exploits the genotype-to-phenotype relationship at the whole-genome level and is being implemented in many crops. Here we show that design-thinking and data-mining techniques can be leveraged to optimize genomic prediction of hybrid performance. We phenotyped a set of 276 maize hybrids generated by crossing founder inbreds of nested association mapping populations for flowering time, ear height, and grain yield. With 10 296 310 SNPs available from the parental inbreds, we explored the patterns of genomic relationships and phenotypic variation to establish training samples based on clustering, graphic network analysis, and genetic mating scheme. Our analysis showed that training set designs outperformed random sampling and earlier methods that either minimize the mean of prediction error variance or maximize the mean of generalized coefficient of determination. Additional analyses of 2556 wheat hybrids from an early-stage hybrid breeding system and 1439 rice hybrids from an established hybrid breeding system validated the approaches. Together, we demonstrated that effective genomic prediction models can be established with a training set 2%-13% of the size of the whole set, enabling an efficient exploration of enormous inference space of genetic combinations.
Pubmed ID: 30625380
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Software package for analysis of large-scale genetic data sets with hundreds of thousands of markers genotyped on thousands of samples. BEAGLE can * phase genotype data (i.e. infer haplotypes) for unrelated individuals, parent-offspring pairs, and parent-offspring trios. * infer sporadic missing genotype data. * impute ungenotyped markers that have been genotyped in a reference panel. * perform single marker and haplotypic association analysis. * detect genetic regions that are homozygous-by-descent in an individual or identical-by-descent in pairs of individuals. Beagle can also be used in conjunction with PRESTO, a program for fast and flexible permutation testing. PRESTO can compute empirical distributions of order statistics, analyze stratified data, and determine significance levels for one-stage and two-stage genetic association studies. BEAGLE is written in Java and runs on any computing platform with a Java version 1.6 interpreter (e.g. Windows, Unix, Linux, Solaris, Mac).
View all literature mentionsA web-based hosting service for software development projects that use the Git revision control system offering powerful collaboration, code review, and code management. It offers both paid plans for private repositories, and free accounts for open source projects. Large or small, every repository comes with the same powerful tools. These tools are open to the community for public projects and secure for private projects. Features include: * Integrated issue tracking * Collaborative code review * Easily manage teams within organizations * Text entry with understated power * A growing list of programming languages and data formats * On the desktop and in your pocket - Android app and mobile web views let you keep track of your projects on the go.
View all literature mentionsIt contains the genotype, phenotype, and polymorphism data produced by the NSF-funded project; Genetic Architecture of Maize and Teosinte. The PANZEA project will identify genes that control domestication traits and three key agronomic traits: flowering time, plant height, and kernel quality. Genetic linkage, association, and fine mapping analyses will be performed on the largest and most diverse set of mapping families publicly available for any species. A large series of isogenic lines will be used to characterize allelic series and epistatic interactions. The genetic architecture of each of the four trait groups will be compared and contrasted, and the influence of recombination and past domestication bottlenecks on the genomic distribution of functional diversity will be examined. Finally, the ability of genetic architecture-based models to predict phenotype will be evaluated in a broad range of germplasm, including elite US hybrids. This project will take a step toward the ultimate goal of predicting phenotype from genotype.
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