High dimensional genomic profiling studies, including DNA methylation, transcriptome sequencing and microbiome, are frequently performed in prospective studies to identify risk factors for disease risk or clinical outcomes. For a complex phenotype, there might be many independent factors, each of which has only modest effect size. To successfully design a study to identify risk factors and to build predictive models, one has to estimate the overall contribution from all predictors. The linear mixed model developed for estimating the heritability in genetic studies can be used for this purpose. However, the measurement error in the genomic predictors may complicate the estimate of the overall. We here develop a statistical method for unbiasedly estimating overall contrition with accuracy verified by extensive simulations. The algorithm will be exemplified in a prospective study investigating the relationship between human oral microbiome and cancer risk.