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X. Lin 197expression, respectively. The gene expression G i (i.e., the mediator) dependson the q covariates (X i ) and the p SNPs (S i ) through a linear model, asG i = X ⊤ i α X + S ⊤ i α S + ɛ i , (18.3)where α X and α S are the regression coefficients for the covariates and theSNPs, respectively. Here, ɛ i follows a Normal distribution with mean 0 andvariance σ 2 G .The total effect (TE) of SNPs of the disease outcome Y can be decomposedinto the Direct Effect (DE) and the Indirect Effect (IE). The Direct Effect ofSNPs is the effect of the SNPs on the disease outcome that is not throughgene expression, whereas the Indirect Effect of the SNPs is the effect of theSNPs on the disease outcome that is through the gene expression. Under nounmeasured confounding assumptions (VanderWeele and Vansteelandt, 2010),the TE, DE and IE can be estimated from the joint causal mediation models(18.2)–(18.3). In genome-wide genetic and genomic studies, the numbers ofSNPs (S) and gene expressions (G) are both large. It is of interest to developmediation analysis methods in such settings.18.5 Training the next generation statistical genetic andgenomic scientists in the ’omics eraTo help expedite scientific discovery in the ’omics era and respond to the pressingquantitative needs for handling massive ’omics data, there is a significantneed to train a new generation of quantitative genomic scientists through anintegrative approach designed to meet the challenges of today’s biomedical science.The traditional biostatistical training does not meet the need. We needto train a cadre of interdisciplinary biostatisticians with strong quantitativeskills and biological knowledge to work at the interface of biostatistics, computationalbiology, molecular biology, and population and clinical science genomics.They will be poised to become quantitative leaders in integrative andteam approaches to genetic research in the public health and medical arenas.Trainees are expected to (1) have strong statistical and computational skillsfor development of statistical and computational methods for massive ’omicsdata and for integration of large genomic data from different sources; (2) havesufficient biological knowledge and understanding of both basic science andpopulation science; (3) work effectively in an interdisciplinary research environmentto conduct translation research from basic science to populationand clinical sciences; (4) play a quantitative leadership role in contributing tofrontier scientific discovery and have strong communication skills to be ableto engage in active discussions of the substance of biomedical research.