Dataset Information

Genomic selection using principal component regression.

ABSTRACT: Many statistical methods are available for genomic selection (GS) through which genetic values of quantitative traits are predicted for plants and animals using whole-genome SNP data. A large number of predictors with much fewer subjects become a major computational challenge in GS. Principal components regression (PCR) and its derivative, i.e., partial least squares regression (PLSR), provide a solution through dimensionality reduction. In this study, we show that PCR can perform better than PLSR in cross validation. PCR often requires extracting more components to achieve the maximum predictive ability than PLSR and thus may be associated with a higher computational cost. However, application of the HAT method (a strategy of describing the relationship between the fitted and observed response variables with a hat matrix) to PCR circumvents conventional cross validation in testing predictive ability, resulting in substantially improved computational efficiency over PLSR where cross validation is mandatory. Advantages of PCR over PLSR are illustrated with a simulated trait of a hypothetical population and four agronomical traits of a rice population. The benefit of using PCR in genomic selection is further demonstrated in an effort to predict 1000 metabolomic traits and 24,973 transcriptomic traits in the same rice population.

SUBMITTER: Du C

PROVIDER: S-EPMC5997717 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Genomic selection using principal component regression.

Du Caroline C Wei Julong J Wang Shibo S Jia Zhenyu Z

Heredity 20180501 1

Many statistical methods are available for genomic selection (GS) through which genetic values of quantitative traits are predicted for plants and animals using whole-genome SNP data. A large number of predictors with much fewer subjects become a major computational challenge in GS. Principal components regression (PCR) and its derivative, i.e., partial least squares regression (PLSR), provide a solution through dimensionality reduction. In this study, we show that PCR can perform better than PL ...[more]

PMID: 29713089

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Project description:BackgroundIdentification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data.Materials and methodsWe have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure.ResultsHere, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes.ConclusionWe show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.

Dataset Information

Genomic selection using principal component regression.

Publications

Genomic selection using principal component regression.

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