Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 541003
Title Genomic prediction using individual-level data and summary statistics from multiple populations
Author(s) Vandenplas, Jeremie; Calus, Mario P.L.; Gorjanc, Gregor
Source Genetics 210 (2018)1. - ISSN 0016-6731 - p. 53 - 69.
DOI https://doi.org/10.1534/genetics.118.301109
Department(s) LR - Animal Breeding & Genomics
WIAS
Animal Breeding and Genetics
Publication type Refereed Article in a scientific journal
Publication year 2018
Availibility Full text available from 2019-09-01
Keyword(s) Genomic prediction - GenPred - Meta-analysis - Quantitative trait - Shared data resources - Statistical method
Abstract

This study presents a method for genomic prediction that uses individual-level data and summary statistics from multiple populations. Genome-wide markers are nowadays widely used to predict complex traits, and genomic prediction using multi-population data are an appealing approach to achieve higher prediction accuracies. However, sharing of individual-level data across populations is not always possible. We present a method that enables integration of summary statistics from separate analyses with the available individual-level data. The data can either consist of individuals with single or multiple (weighted) phenotype records per individual. We developed a method based on a hypothetical joint analysis model and absorption of population-specific information. We show that population-specific information is fully captured by estimated allele substitution effects and the accuracy of those estimates, i.e., the summary statistics. The method gives identical result as the joint analysis of all individual-level data when complete summary statistics are available. We provide a series of easy-to-use approximations that can be used when complete summary statistics are not available or impractical to share. Simulations show that approximations enable integration of different sources of information across a wide range of settings, yielding accurate predictions. The method can be readily extended to multiple-traits. In summary, the developed method enables integration of genome-wide data in the individual-level or summary statistics from multiple populations to obtain more accurate estimates of allele substitution effects and genomic predictions.

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