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      Reliability of genomic predictions across multiple populations.

      1 , ,
      Genetics
      Genetics Society of America

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          Abstract

          Genomic prediction of future phenotypes or genetic merit using dense SNP genotypes can be used for prediction of disease risk, forensics, and genomic selection of livestock and domesticated plant species. The reliability of genomic predictions is their squared correlation with the true genetic merit and indicates the proportion of the genetic variance that is explained. As reliability relies heavily on the number of phenotypes, combining data sets from multiple populations may be attractive as a way to increase reliabilities, particularly when phenotypes are scarce. However, this strategy may also decrease reliabilities if the marker effects are very different between the populations. The effect of combining multiple populations on the reliability of genomic predictions was assessed for two simulated cattle populations, A and B, that had diverged for T = 6, 30, or 300 generations. The training set comprised phenotypes of 1000 individuals from population A and 0, 300, 600, or 1000 individuals from population B, while marker density and trait heritability were varied. Adding individuals from population B to the training set increased the reliability in population A by up to 0.12 when the marker density was high and T = 6, whereas it decreased the reliability in population A by up to 0.07 when the marker density was low and T = 300. Without individuals from population B in the training set, the reliability in population B was up to 0.77 lower than in population A, especially for large T. Adding individuals from population B to the training set increased the reliability in population B to close to the same level as in population A when the marker density was sufficiently high for the marker-QTL linkage disequilibrium to persist across populations. Our results suggest that the most accurate genomic predictions are achieved when phenotypes from all populations are combined in one training set, while for more diverged populations a higher marker density is required.

          Most cited references15

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          Mapping genes for complex traits in domestic animals and their use in breeding programmes.

          Genome-wide panels of SNPs have recently been used in domestic animal species to map and identify genes for many traits and to select genetically desirable livestock. This has led to the discovery of the causal genes and mutations for several single-gene traits but not for complex traits. However, the genetic merit of animals can still be estimated by genomic selection, which uses genome-wide SNP panels as markers and statistical methods that capture the effects of large numbers of SNPs simultaneously. This approach is expected to double the rate of genetic improvement per year in many livestock systems.
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            Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

            Background The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.
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              Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle.

              When a genetic marker and a quantitative trait locus (QTL) are in linkage disequilibrium (LD) in one population, they may not be in LD in another population or their LD phase may be reversed. The objectives of this study were to compare the extent of LD and the persistence of LD phase across multiple cattle populations. LD measures r and r(2) were calculated for syntenic marker pairs using genomewide single-nucleotide polymorphisms (SNP) that were genotyped in Dutch and Australian Holstein-Friesian (HF) bulls, Australian Angus cattle, and New Zealand Friesian and Jersey cows. Average r(2) was approximately 0.35, 0.25, 0.22, 0.14, and 0.06 at marker distances 10, 20, 40, 100, and 1000 kb, respectively, which indicates that genomic selection within cattle breeds with r(2) >or= 0.20 between adjacent markers would require approximately 50,000 SNPs. The correlation of r values between populations for the same marker pairs was close to 1 for pairs of very close markers (<10 kb) and decreased with increasing marker distance and the extent of divergence between the populations. To find markers that are in LD with QTL across diverged breeds, such as HF, Jersey, and Angus, would require approximately 300,000 markers.
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                Author and article information

                Journal
                Genetics
                Genetics
                Genetics Society of America
                1943-2631
                0016-6731
                Dec 2009
                : 183
                : 4
                Affiliations
                [1 ] Biosciences Research Division, Department of Primary Industries Victoria, University of Melbourne, Bundoora 3083, Australia. sander.de.roos@crv4all.com
                Article
                genetics.109.104935
                10.1534/genetics.109.104935
                2787438
                19822733
                51f5bf75-f764-4723-b2ab-8f4fdd40b67d
                History

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