Abstract
The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, Medicago truncatula, using geographical locations as covariates for admixture analysis. The method’s prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.
Highlights
Living organisms adapt to the changing environment
We conducted a three-step data analysis for the determination of admixture components that (1) defines an initial most likely range of admixture components by minimizing the cross-validation error of admixture [62] analysis; (2) compares this initial guess of admixture components to an independent analysis using the discriminant analysis of principal components (DAPC) method [63], working by optimizing the ratio of the variance between groups to the variance within groups; and (3) checks the accuracy of bio-geographic predictions for various numbers of components using our new ProvenancePredictor algorithm based on the outcome of steps 1 and 2 (Fig. 1)
This study demonstrates the rationale of our WhoGEM prediction machine: population admixture integrates the effects of demography and of natural selection toward adaptation and thereby explains more phenotypic variation than genomic selection (GS)- or QTLbased approaches
Summary
Living organisms adapt to the changing environment. Species respond to environmental changes by altering population structure via migration, by allele sorting due to random events (genetic drift), and by natural selection [1]. A large number of mutations of small effect is considered to model the phenotypic effect-size distribution of evolutionary-relevant mutations [4,5,6] These evolutionary relevant mutations will likely be key for breeding for complex traits [7], such as fast adaptation to anticipated climate changes in plants or animals, or for Unlike animals, plants feature complex mating systems including selfing and limited gene dispersal through seeds and pollen and a distinct immune system. Plants must survive under permanent selective pressure from local environmental conditions These features make plants excellent subjects to test polygenic adaptation hypotheses and to evaluate the role of migration and drift in the genetic and quantitative phenotypic differentiation among populations [9]. Contradictory versions of its population structure have been described [15,16,17]
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