A general method for controlling the genome-wide type I error rate in linkage and association mapping experiments in plants

Müller, Bettina U.; Stich, Benjamin; Piepho, Hans‐Peter

doi:10.1038/hdy.2010.125

Cited by 29 publications

(26 citation statements)

References 36 publications

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“…65. Alternatively, simulation-based approaches have been proposed for LMMs 57,58 . Provided that effects of independent noise and population structure can be correctly estimated from the data, these methods might help to further improve statistical power/runtime as compared with permutation-based approaches.…”

Section: Methodsmentioning

confidence: 99%

A random forest approach to capture genetic effects in the presence of population structure

2015

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The accurate mapping of causal variants in genome-wide association studies requires the consideration of both, confounding factors (for example, population structure) and nonlinear interactions between individual genetic variants. Here, we propose a method termed 'mixed random forest' that simultaneously accounts for population structure and captures nonlinear genetic effects. We test the model in simulation experiments and show that the mixed random forest approach improves detection power compared with established approaches. In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested. Our results show that approaches that simultaneously account for both, confounding due to population structure and epistatic interactions, are important to fully explain the heritable component of complex quantitative traits.

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Section: Methodsmentioning

confidence: 99%

A random forest approach to capture genetic effects in the presence of population structure

2015

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“…It is from this test that the statistical significance of a fixed marker effect is obtained. However, the high computational cost of permutation and its inability to account for familial relatedness appropriately (Muller et al 2011) renders it unsuitable for managing false positives in genome-wide association studies. If the p values are not adjusted correctly for multiple testing, the type I error rate becomes inflated.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association mapping in plants

George

Cavanagh

2015

Theor Appl Genet

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We present new association mapping methods which address the unique challenges of analyzing genome-wide data from multi-environment plant studies. Association studies on a genome-wide scale are being performed in plants. Unlike human studies, plant studies contain replicates whose data may be recorded across different environments. Plant studies also often employ elaborate experimental designs for controlling extraneous phenotypic variation. As a result, the genome-wide analysis of data from plant studies can be challenging. In this paper, we present QK-based association mapping for the analysis of data from plant association studies. In doing so, we have developed: (a) a general multivariate QK framework for association mapping in plant studies of arbitrary complexity; (b) a new weighted two-stage analysis approach for QK-based association mapping; (c) a heuristic procedure for determining when two-stage analysis is appropriate; and (d) a Monte Carlo sampling procedure for controlling the genome-wide type I error rate. We conduct a simulation study to evaluate the performance of our genome-wide mapping technique. We also analyze data from a multi-environment association study in wheat.

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“…Several authors have considered the issue of multiple testing and have sought ways to address it in genetic studies [52–57]. One solution is to determine the “true” number of independent SNPs, so that the multiple testing correction is not as egregious.…”

Section: Introductionmentioning

confidence: 99%

Single-Variant and Multi-Variant Trend Tests for Genetic Association with Next-Generation Sequencing That Are Robust to Sequencing Error

et al. 2012

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As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTT_ae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTT_ae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTT_ae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTT_ae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.

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A general method for controlling the genome-wide type I error rate in linkage and association mapping experiments in plants

Cited by 29 publications

References 36 publications

A random forest approach to capture genetic effects in the presence of population structure

A random forest approach to capture genetic effects in the presence of population structure

Genome-wide association mapping in plants

Single-Variant and Multi-Variant Trend Tests for Genetic Association with Next-Generation Sequencing That Are Robust to Sequencing Error

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