A sorghum practical haplotype graph facilitates genome-wide imputation and cost-effective genomic prediction.

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome-wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage-only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57-.73 and are similar to prediction accuracies obtained with genotyping-by-sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low-coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost-effective option for genomic selection.

TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline.

Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline, TASSEL-GBS, designed for the efficient processing of raw GBS sequence data into SNP genotypes. The TASSEL-GBS pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8-16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals (e.g., for large breeding programs or genetic surveys), and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished "pseudo-reference" consisting of numerous contigs. We describe the TASSEL-GBS pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the TASSEL-GBS pipeline provide robust tools for studying genomic diversity.

Empirical comparison of Simple Sequence Repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness.

While Simple Sequence Repeats (SSRs) are extremely useful genetic markers, recent advances in technology have produced a shift toward use of single nucleotide polymorphisms (SNPs). The different mutational properties of these two classes of markers result in differences in heterozygosities and allele frequencies that may have implications for their use in assessing relatedness and evaluation of genetic diversity. We compared analyses based on 89 SSRs (primarily dinucleotide repeats) to analyses based on 847 SNPs in individuals from the same 259 inbred maize lines, which had been chosen to represent the diversity available among current and historic lines used in breeding. The SSRs performed better at clustering germplasm into populations than did a set of 847 SNPs or 554 SNP haplotypes, and SSRs provided more resolution in measuring genetic distance based on allele-sharing. Except for closely related pairs of individuals, measures of distance based on SSRs were only weakly correlated with measures of distance based on SNPs. Our results suggest that 1) large numbers of SNP loci will be required to replace highly polymorphic SSRs in studies of diversity and relatedness and 2) relatedness among highly-diverged maize lines is difficult to measure accurately regardless of the marker system.

Genetic diversity and population structure of teosinte.

The teosintes, the closest wild relatives of maize, are important resources for the study of maize genetics and evolution and for plant breeding. We genotyped 237 individual teosinte plants for 93 microsatellites. Phylogenetic relationships among species and subspecific taxa were largely consistent with prior analyses for other types of molecular markers. Plants of all species formed monophyletic clades, although relationships among species were not fully resolved. Phylogenetic analysis indicated that the Mexican annual teosintes divide into two clusters that largely correspond to the previously defined subspecies, Z. mays ssp. parviglumis and ssp. mexicana, although there are a few samples that represent either evolutionary intermediates or hybrids between these two subspecies. The Mexican annual teosintes show genetic substructuring along geographic lines. Hybridization or introgression between some teosintes and maize occurs at a low level and appears most common with Z. mays ssp. mexicana. Phylogeographic and phylogenetic analyses of the Mexican annual teosintes indicated that ssp. parviglumis diversified in the eastern part of its distribution and spread from east to west and that ssp. mexicana diversified in the Central Plateau of Mexico and spread along multiple paths to the north and east. We defined core sets of collections of Z. mays ssp. mexicana and ssp. parviglumis that attempt to capture the maximum number of microsatellite alleles for given sample sizes.