As the examples in this chapter and throughout the book illustrate, genetic analysis is the foundation of our understanding of many fundamental processes in cell biology. By examining the phenotypic consequences of mutations that inactivate a particular gene, geneticists are able to connect knowledge about the sequence, structure, and biochemical activity of the encoded protein to its function in the context of a living cell or multicellular organism. The classical approach to making these connections in both humans and simpler, experimentally accessible organisms has been to identify new mutations of interest based on their phenotypes and then to isolate the affected gene and its protein product.

Although scientists continue to use this classical genetic approach to dissect fundamental cellular processes and biochemical pathways, the availability of complete genomic sequence information for most of the common experimental organisms has fundamentally changed the way genetic experiments are conducted. Using various computational methods, scientists have identified the protein-coding gene sequences in most of these experimental organisms, including E. coli, yeast, C. elegans, Drosophila, Arabidopsis, and mouse, as well as in human. The gene sequences, in turn, reveal the primary amino acid sequences of the encoded protein products, providing us with a nearly complete list of the proteins found in each of the major experimental organisms.

The approach taken by most researchers has thus shifted from discovering new genes and proteins to discovering the functions of genes and proteins whose sequences are already known. Once an interesting gene has been identified, genomic sequence information greatly speeds subsequent genetic manipulations of the gene, including its inactivation, so that more can be learned about its function. Already sets of vectors for RNAi inactivation of most defined genes in the nematode C. elegans allow efficient genetic screens to be performed in this multicellular organism. These methods are now being applied to large collections of genes in cultured mammalian cells, and in the near future, either RNAi or knockout methods will have been used to inactivate every gene in the mouse.

Human genetic mapping methods, such as genome-wide association studies and linkage disequilibrium studies, applied to large populations have identified a large number of alleles in candidate genes that show a statistical tendency to predispose people to one or another polygenic inherited disease. With the advent of a powerful genome editing tool based on the bacterial CRISPR system, it should now be possible to test the effects of many of these allelic variants in a model organism such as the mouse. These ambitious studies open the way to a new era in the functional analysis of genomes. We can expect to begin to understand the function of a majority of human genes not only on the basis of the effect of a complete absence of gene function, but also across a range of more subtle differences in function that span the allelic variation that exists in human populations.

In the past, a scientist might spend many years studying a single gene, but today scientists commonly study whole sets of genes at once. For example, with DNA microarrays, the levels of expression of all genes in an organism can be measured almost as easily as the expression of a single gene. One of the great challenges facing geneticists in the twenty-first century is to exploit the vast amount of available data on the function and regulation of individual genes to understand how groups of genes in their various allelic forms are organized to form complex biochemical pathways and regulatory networks.