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Confirmatory Results

Optimizing the identification of causal variants across varying genetic architectures in crops

View ORCID ProfileChenyong Miao, View ORCID ProfileJinliang Yang, View ORCID ProfileJames C. Schnable
doi: https://doi.org/10.1101/310391
Chenyong Miao
1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, 68503 Lincoln, NE, U.S
2Center for Plant Science Innovation, University of Nebraska-Lincoln, 68503 Lincoln, NE, U.S
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Jinliang Yang
1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, 68503 Lincoln, NE, U.S
2Center for Plant Science Innovation, University of Nebraska-Lincoln, 68503 Lincoln, NE, U.S
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James C. Schnable
1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, 68503 Lincoln, NE, U.S
2Center for Plant Science Innovation, University of Nebraska-Lincoln, 68503 Lincoln, NE, U.S
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  • For correspondence: schnable@unl.edu
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Abstract

Background Association studies use statistical links between genetic markers and variation in a phenotype’s value across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome-wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures.

Results Here we employ simulation studies utilizing real-world genotype datasets from association populations in four species with distinct minor allele frequency distributions, population structures, and patterns linkage disequilibrium to evaluate the impact of variation in both heritability and trait complexity on both conventional mixed linear model based GWAS and two new approaches specifically developed for complex traits. Mixed linear model based GWAS rapidly losses power for more complex traits. FarmCPU, a method based on multi-locus mixed linear models, provides the greatest statistical power for moderately complex traits. A Bayesian approach adopted from genomic prediction provides the greatest statistical power to identify causal genetic loci for extremely complex traits.

Conclusions Using estimates of the complexity of the genetic architecture of target traits can guide the selection of appropriate statistical methods and improve the overall accuracy and power of GWAS.

  • List of abbreviations

    GWAS
    : Genome-Wide Association Study
    GBS
    : Genotyping-By-Sequencing
    PCA
    : Principal Component Analysis
    LD
    : Linkage Disequilibrium
    SNP
    : Single Nucleotide Polymorphism
    MAF
    : Minor Allele Frequency
    QTN
    : Quantitative Trait Nucleotide
    GEMMA
    : Genomic Association and Prediction Integrated Tool
    GLM
    : General Linear Model
    MLM
    : Mixed Linear Model
    MLMM
    : Multi-Locus Mixed-Model
    FDR
    : False Discovery Rate
    HDRA
    : High-Density Rice Array
    HCC
    : the Holland Computing Center
  • Copyright 
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    Posted May 02, 2018.
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    Optimizing the identification of causal variants across varying genetic architectures in crops
    Chenyong Miao, Jinliang Yang, James C. Schnable
    bioRxiv 310391; doi: https://doi.org/10.1101/310391
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    Optimizing the identification of causal variants across varying genetic architectures in crops
    Chenyong Miao, Jinliang Yang, James C. Schnable
    bioRxiv 310391; doi: https://doi.org/10.1101/310391

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