High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker-trait associations in mapping experiments. We developed a genotyping array including about 90,000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence-absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.

/pdf/characterization-of-polyploid-wheat-genomic-diversity-using-2k5bjm1mv0.pdf

Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array

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.

/pdf/tassel-gbs-a-high-capacity-genotyping-by-sequencing-analysis-48u7fd4437.pdf

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

Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.

https://horticulture.wisc.edu/wp-content/uploads/sites/20/2013/09/GWAS-Review-2013-11Nov131.pdf

The advantages and limitations of trait analysis with GWAS: a review

A high-density haplotype map recently enabled a genome-wide association study (GWAS) in a population of indica subspecies of Chinese rice landraces. Here we extend this methodology to a larger and more diverse sample of 950 worldwide rice varieties, including the Oryza sativa indica and Oryza sativa japonica subspecies, to perform an additional GWAS. We identified a total of 32 new loci associated with flowering time and with ten grain-related traits, indicating that the larger sample increased the power to detect trait-associated variants using GWAS. To characterize various alleles and complex genetic variation, we developed an analytical framework for haplotype-based de novo assembly of the low-coverage sequencing data in rice. We identified candidate genes for 18 associated loci through detailed annotation. This study shows that the integrated approach of sequence-based GWAS and functional genome annotation has the potential to match complex traits to their causal polymorphisms in rice.

Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm.

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.

/pdf/iterative-usage-of-fixed-and-random-effect-models-for-1azej5qm30.pdf

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

US maize yield has increased eight-fold in the past 80 years, with half of the gain attributed to selection by breeders. During this time, changes in maize leaf angle and size have altered plant architecture, allowing more efficient light capture as planting density has increased. Through a genome-wide association study (GWAS) of the maize nested association mapping panel, we determined the genetic basis of important leaf architecture traits and identified some of the key genes. Overall, we demonstrate that the genetic architecture of the leaf traits is dominated by small effects, with little epistasis, environmental interaction or pleiotropy. In particular, GWAS results show that variations at the liguleless genes have contributed to more upright leaves. These results demonstrate that the use of GWAS with specially designed mapping populations is effective in uncovering the basis of key agronomic traits.

http://web.missouri.edu/~flint-garcias/GWASLeafAngle.pdf

Genome-wide association study of leaf architecture in the maize nested association mapping population

The fungus Fusarium verticillioides infects maize (Zea mays L.) ears and kernels, resulting in Fusarium ear rot disease, reduced grain yields, and contamination of grain with the mycotoxin fumonisin. Hybrid maize breeding programs involve selection for both inbred and hybrid performance; the emphasis placed on inbred versus hybrid selection depends on heritability of and the genetic correlation between resistance in the different generations. The objectives of this study were to assess the importance of general combing ability (GCA) and specifi c combining ability (SCA) for resistance to Fusarium ear rot and fumonisin contamination and to estimate the correlation between resistance measured in inbred and hybrid generations. We evaluated a diallel mating of 18 inbred lines from different heterotic groups with different levels of resistance. Hybrids had 27% less ear rot and 30% less fumonisin content than their inbred parents, demonstrating the importance of hybrid vigor to disease resistance. Both GCA and SCA were signifi cant for yield and disease resistance, and inbred performance per se and corresponding GCA in hybrids were signifi cantly correlated (r ≥ 0.78). Genetic variation for resistance was greater in inbred lines than among hybrids, however. These results suggest that the most effi cient way to improve Fusarium ear rot and fumonisin contamination resistances in hybrids is to evaluate and select among inbred lines before using resources to create and evaluate hybrids.

Hsiao-Yi Hung

Papers

Genome-wide association study of leaf architecture in the maize nested association mapping population

Diallel Analysis of Resistance to Fusarium Ear Rot and Fumonisin Contamination in Maize