**1. Introduction**

One of the major objectives of modern biology and medicine is prediction: being able to take information about an individual's genome and environment and accurately predict their phenotype. This effort has taken on many forms and many names in different fields over time including population genetics [1], statistical genetics, quantitative genetics [2], genetical genomics [3], complex trait analysis [4], systems genetics [5, 6], systems medicine [7, 8], personalized medicine [9], predictive medicine and precision medicine [10, 11]. In humans, this has been greatly constrained by the *N*-of-1 problem, by which we mean that each person is a unique individual [12] – even monozygotic twins will differ in their environment. This has made it impractical, if not impossible, to accurately predict at the individual level disease risk or best treatment options for most common diseases, especially across populations [13–18], although we can, of course, make generalizations within a population. As sample sizes for genome-wide association studies have grown, it has become increasingly clear that any single commonly segregating variant is likely to have a very small impact on disease risk [19–21]. Indeed, an omnigenic model has been proposed, whereby variants in every gene are likely to affect every phenotype [22]. Even for Mendelian disorders such as Huntington's disease, there are other alleles in the genetic background which modulate age of onset [23].

How then, if there is so much complication in this one-to-one relationship (one gene variant to one phenotype), can we uncover the true many-to-many-to-many relationships that occur in biology? Phenotypes at many levels, including behavior, organ systems, cells, proteins, metabolites, and mRNAs, all interact together with sets of many gene variants, and with an individual's current and previous environmental exposures. We need to understand gene–gene (epistasis), gene-age, gene-sex, gene-treatment, and gene–environment interactions and all their combinations. One answer to this is through the use of recombinant inbred (RI) populations and their derivatives.
