Genomic Prediction Using Individual-Level Data and Summary Statistics from Multiple Populations. Issue 1 (18th July 2018)

• Record Type: Journal Article
• Authors: Vandenplas, Jeremie; Calus, Mario P L; Gorjanc, Gregor
• Is Part Of: Genetics. Volume 210:Issue 1(2018)
• Page Start: 53
• Page End: 69
• Publication Date: 2018-07-18
• Subjects: meta-analysis -- quantitative trait -- statistical method -- Genomic Prediction -- GenPred -- Shared Data Resources; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.118.301109
• Languages: English
• ISSNs: 0016-6731

Abstract

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.