Dataset Information

Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize.

ABSTRACT: Association mapping (AM) is a powerful tool for fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events. A major problem in AM is controlling false positives that can arise from population structure and family relatedness. False positives are often controlled by incorporating covariates for structure and kinship in mixed linear models (MLM). These MLM-based methods are single locus models and can introduce false negatives due to over fitting of the model. In this study, eight different statistical models, ranging from single-locus to multilocus, were compared for AM for three traits differing in heritability in two crop species: soybean (Glycine max L.) and maize (Zea mays L.). Soybean and maize were chosen, in part, due to their highly differentiated rate of linkage disequilibrium (LD) decay, which can influence false positive and false negative rates. The fixed and random model circulating probability unification (FarmCPU) performed better than other models based on an analysis of Q-Q plots and on the identification of the known number of quantitative trait loci (QTLs) in a simulated data set. These results indicate that the FarmCPU controls both false positives and false negatives. Six qualitative traits in soybean with known published genomic positions were also used to compare these models, and results indicated that the FarmCPU consistently identified a single highly significant SNP closest to these known published genes. Multiple comparison adjustments (Bonferroni, false discovery rate, and positive false discovery rate) were compared for these models using a simulated trait having 60% heritability and 20 QTLs. Multiple comparison adjustments were overly conservative for MLM, CMLM, ECMLM, and MLMM and did not find any significant markers; in contrast, ANOVA, GLM, and SUPER models found an excessive number of markers, far more than 20 QTLs. The FarmCPU model, using less conservative methods (false discovery rate, and positive false discovery rate) identified 10 QTLs, which was closer to the simulated number of QTLs than the number found by other models.

SUBMITTER: Kaler AS

PROVIDER: S-EPMC7052329 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize.

Kaler Avjinder S AS Gillman Jason D JD Beissinger Timothy T Purcell Larry C LC

Frontiers in plant science 20200225

Association mapping (AM) is a powerful tool for fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events. A major problem in AM is controlling false positives that can arise from population structure and family relatedness. False positives are often controlled by incorporating covariates for structure and kinship in mixed linear models (MLM). These MLM-based methods are single locus models and can introduce false negatives due to over fittin ...[more]

PMID: 32158452

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Project description:Genetically modified (GM) crops are one of the most valuable tools of modern biotechnology that secure yield potential needed to sustain the global agricultural demands for food, feed, fiber, and energy. Crossing single GM events through conventional breeding has proven to be an effective way to pyramid GM traits from individual events and increase yield protection in the resulting combined products. Even though years of research and commercialization of GM crops show that these organisms are safe and raise no additional biosafety concerns, some regulatory agencies still require risk assessments for these products. We sought out to investigate whether stacking single GM events would have a significant impact on agronomic and phenotypic plant characteristics in soybean, maize, and cotton. Several replicated field trials designed as randomized complete blocks were conducted by Monsanto Regulatory Department from 2008 to 2017 in field sites representative of cultivation regions in Brazil. In total, twenty-one single and stacked GM materials currently approved for in-country commercial use were grown with the corresponding conventional counterparts and commercially available GM/non-GM references. The generated data were presented to the Brazilian regulatory agency CTNBio (National Biosafety Technical Committee) over the years to request regulatory approvals for the single and stacked products, in compliance with the existing normatives. Data was submitted to analysis of variance and differences between GM and control materials were assessed using t-test with a 5% significance level. Data indicated the predominance of similarities and neglectable differences between single and stacked GM crops when compared to conventional counterpart. Our results support the conclusion that combining GM events through conventional breeding does not alter agronomic or phenotypic plant characteristics in these stacked crops. This is compatible with a growing weight of evidence that indicates this long-adopted strategy does not increase the risks associated with GM materials. It also provides evidence to support the review and modernization of the existing regulatory normatives to no longer require additional risk assessments of GM stacks comprised of previously approved single events for biotechnology-derived crops. The data analyzed confirms that the risk assessment of the individual events is sufficient to demonstrate the safety of the stacked products, which deliver significant benefits to growers and to the environment.

Dataset Information

Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize.

Publications

Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize.

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