Abstract
Genome-wide association studies have helped us identify a wealth of genetic variants associated with complex human phenotypes. Because most variants explain a small portion of the total phenotypic variation, however, marker-based studies remain limited in their ability to predict such phenotypes. Here, we show how modern statistical genetic techniques borrowed from animal breeding can be employed to increase the accuracy of genomic prediction of complex phenotypes and the power of genetic mapping studies.Specifically, using the triglyceride data of the GAW20 data set, we apply genomic-best linear unbiased prediction (G-BLUP) methods to obtain empirical genetic values (EGVs) for each triglyceride phenotype and each individual. We then study 2 different factors that influence the prediction accuracy of G-BLUP for the analysis of human data: (a) the choice of kinship matrix, and (b) the overall level of relatedness. The resulting genetic values represent the total genetic component for the phenotype of interest and can be used to represent a trait without its environmental component.Finally, using empirical data, we demonstrate how this method can be used to increase the power of genetic mapping studies. In sum, our results show that dense genome-wide data can be used in a wider scope than previously anticipated.
Highlights
Genomic prediction (GP) refers to the use of genomic information for predicting an individual’s phenotype [1]
Accuracy of TG empirical genetic values (EGVs) pre- and posttreatment Accuracy estimates (Fig. 1) suggest that both pre- and posttreatment TG levels can be fairly accurately predicted based on their kinship matrices, regardless of whether IBDLD, LDAK, or pedigree kinship was used
Association between EGV reliability and the number of relatives in the pedigree When regressing accuracy estimates on the number of second-degree relatives (SDRs) (Fig. 2), we found a close relationship between the accuracy of EGV estimates and the number of relatives an individual possesses in the pedigree
Summary
Genomic prediction (GP) refers to the use of genomic information for predicting an individual’s phenotype [1]. Several different approaches have been developed with the purpose of performing GP, such as marker-assisted selection (MAS) and genomic-best linear unbiased prediction methods (G-BLUP) [2]. MAS approaches have been widely successful when single genomic variants affect the trait of interest, but remain limited in their predictive capabilities for complex phenotypes [3]. Evidence suggests that complex traits are influenced by many genes, with effects that often fall below statistical significance thresholds [4]. The combined effects of variants identified through association only explains a small portion of the interindividual phenotypic differences [5].
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