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The promise of personalized genomic medicine is that knowledge of a person's gene sequences and activity will facilitate more appropriate medical interventions, particularly drug prescriptions, to reduce the burden of disease. Early successes in oncology and pediatrics have affirmed the power of positive diagnosis and are mostly based on detection of one or a few mutations that drive the specific pathology. However, genetically more complex diseases require the development of polygenic risk scores (PRSs) that have variable accuracy. The rarity of events often means that they have necessarily low precision: many called positives are actually not at risk, and only a fraction of cases are prevented by targeted therapy. In some situations, negative prediction may better define the population at low risk. Here, I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription. This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity but also with respect to potential clinical efficacy.

There is increasing evidence that heritable variation in gene expression underlies genetic variation in susceptibility to disease. Therefore, a comprehensive understanding of the similarity between relatives for transcript variation is warranted--in particular, dissection of phenotypic variation into additive and non-additive genetic factors and shared environmental effects. We conducted a gene expression study in blood samples of 862 individuals from 312 nuclear families containing MZ or DZ twin pairs using both pedigree and genotype information. From a pedigree analysis we show that the vast majority of genetic variation across 17,994 probes is additive, although non-additive genetic variation is identified for 960 transcripts. For 180 of the 960 transcripts with non-additive genetic variation, we identify expression quantitative trait loci (eQTL) with dominance effects in a sample of 339 unrelated individuals and replicate 31% of these associations in an independent sample of 139 unrelated individuals. Over-dominance was detected and replicated for a trans association between rs12313805 and ETV6, located 4MB apart on chromosome 12. Surprisingly, only 17 probes exhibit significant levels of common environmental effects, suggesting that environmental and lifestyle factors common to a family do not affect expression variation for most transcripts, at least those measured in blood. Consistent with the genetic architecture of common diseases, gene expression is predominantly additive, but a minority of transcripts display non-additive effects.

We describe a novel approach to capturing the covariance structure of peripheral blood gene expression that relies on the identification of highly conserved Axes of variation. Starting with a comparison of microarray transcriptome profiles for a new dataset of 189 healthy adult participants in the Emory-Georgia Tech Center for Health Discovery and Well-Being (CHDWB) cohort, with a previously published study of 208 adult Moroccans, we identify nine Axes each with between 99 and 1,028 strongly co-regulated transcripts in common. Each axis is enriched for gene ontology categories related to sub-classes of blood and immune function, including T-cell and B-cell physiology and innate, adaptive, and anti-viral responses. Conservation of the Axes is demonstrated in each of five additional population-based gene expression profiling studies, one of which is robustly associated with Body Mass Index in the CHDWB as well as Finnish and Australian cohorts. Furthermore, ten tightly co-regulated genes can be used to define each Axis as "Blood Informative Transcripts" (BITs), generating scores that define an individual with respect to the represented immune activity and blood physiology. We show that environmental factors, including lifestyle differences in Morocco and infection leading to active or latent tuberculosis, significantly impact specific axes, but that there is also significant heritability for the Axis scores. In the context of personalized medicine, reanalysis of the longitudinal profile of one individual during and after infection with two respiratory viruses demonstrates that specific axes also characterize clinical incidents. This mode of analysis suggests the view that, rather than unique subsets of genes marking each class of disease, differential expression reflects movement along the major normal Axes in response to environmental and genetic stimuli.

The different environments that humans experience are likely to impact physiology and disease susceptibility. In order to estimate the magnitude of the impact of environment on transcript abundance, we examined gene expression in peripheral blood leukocyte samples from 46 desert nomadic, mountain agrarian and coastal urban Moroccan Amazigh individuals. Despite great expression heterogeneity in humans, as much as one third of the leukocyte transcriptome was found to be associated with differences among regions. Genome-wide polymorphism analysis indicates that genetic differentiation in the total sample is limited and is unlikely to explain the expression divergence. Methylation profiling of 1,505 CpG sites suggests limited contribution of methylation to the observed differences in gene expression. Genetic network analysis further implies that specific aspects of immune function are strongly affected by regional factors and may influence susceptibility to respiratory and inflammatory disease. Our results show a strong genome-wide gene expression signature of regional population differences that presumably include lifestyle, geography, and biotic factors, implying that these can play at least as great a role as genetic divergence in modulating gene expression variation in humans.