Beyond missing heritability: prediction of complex traits

Abstract

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the "missing heritability" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼ 0.80), substantial room for improvement remains.

Figure 1. A simplified representation of assessment of goodness of fit in a training dataset and of predictive ability across a population: an example with the Framingham population.

Figure 2. We averaged the estimates of (measured in the training data), , (measured in a 10 fold cross validation), (measured in a 2 generation validation), and (measured in a replicated Training-Testing validation) over the three modeling techniques (BL, G^H, G^Y) and showed their relationship to the number of SNPs included in the model.

Figure 3. Averaged (across the three different models) estimates of (measured in a 10 fold cross validation) while varying the number of close relatives (s_i) in the training dataset with 2.5K to 400K SNPs.