As 2025 approaches, we can expect the silver anniversary announcements on the completion of a draft of the human genome to be on their way. Many of the people who were involved are still alive and well known. Because of this, we will likely hear reflections from an ensemble cast of characters associated with the 2000 announcement, and those whose more contemporary work is linked to the study of genomes: J. Craig Venter, Francis Collins, Jennifer Doudna, and others. We have entered what can be called a “post-genomic” age, where the biological sciences build on our understanding, developed over the past quarter-century, moving us towards the next generation of discoveries in various subfields of biology.
What work is “post” doing in “post-genomic?” One dictionary definition offers that “post” can be used as “a prefix, meaning ‘behind,’ ‘after,’ ‘later,’ ‘subsequent to,’ ‘posterior to.’” Its use in “post-genomic” does not indicate a world without genomics, but rather a scientific world where we take genomics for granted and it is no longer the bottleneck in understanding biological systems at the molecular level.
Today, the act of sequencing and analyzing genomic data is rarely the rate-limiting step for many endeavors: Biotechnology and big data have flooded virtually every field related to biology — from paleontology to medical genetics — with genomic data that provide a critical window into how life evolved and, to a large degree, how it functions. To be clear, we still have a long way to go in basic genomic analysis for most recognized (and undiscovered) species on Earth. But the cost of sequencing has decreased even more dramatically than predicted by Moore’s Law. And the tools to analyze genomes are no longer confined to those who can write code in Perl (one of the early computing languages used for bioinformatics). In 2024, the tools to study genomes are so automated that one doesn’t need to understand computer science at any level of sophistication to make nontrivial contributions to genomic science. Even my cynical soul struggles to come up with reasons why this isn’t a good thing. Accessibility is better.
In today’s world, post-genomic is built on two important ideas: that, as previously mentioned, doing genomics is easier than ever, and that genetic information is not enough. Post-genomic embodies a world where we can and should focus on the next big (or small) revolutionary ideas in the study of the biological world. Here, I’ll briefly outline two areas of inquiry that help to define the next era of discoveries.
The goal of resolving gene-environment interactions — how different contexts and circumstances influence how genes do their job — is both one of the most important areas in all of biology and one that is increasingly hard to interpret and apply. An entire subfield of studies now examines how subtleties of the environment shape genomic information. For example, plant biologists have resolved how temperature and genes influence the height of sorghum. And in humans, modern studies demonstrate the potential power of directly studying how environments shape genomes and their products.
In a recent study, an international collaboration of scientists discovered how environmental exposures may explain certain ethnic health disparities in cancer. Specifically, the team found that tumors from self-reported Black patients show an elevated signature of whole-genome duplications, where entire chromosomes double inside of cells (a major pattern in cancer). But more than simply highlighting a molecular difference across racial identities, the authors took the critical step of measuring how combustion byproducts — associated with poor air quality in real-world settings, the result of exposure to carcinogens from factories and highways — can foster these whole-genome duplications. This type of study is only possible in an era when genomic technology is relatively inexpensive. But it offers a careful and rigorous way to invoke the role of environmental forces in shaping genomic features associated with disparate health outcomes. And even more, it highlights the overlap between social forces (those that expose populations to poor air quality, in this case) and genomics.
In today’s world, post-genomic is built on two important ideas: that doing genomics is easier than ever, and that genetic information is not enough.
The cancer genetics example outlined above shows how one can invoke established genomics methods, environmental stressors, and appreciation of social context to address important problems, such as ethnic disparities in cancer outcomes. But what about basic conceptual and theoretical ideas in genetics and genomics? The theoretical foundations of fields like population genetics were developed in an era when we knew little about how genes functioned or how genomes were constructed. Now, awash in petabytes to exabytes of genomic data on servers across the world, the newest challenge in a post-genomic world might involve how to make sense of this data and move towards a cohesive picture of the molecular bases of life. And this might, counter-intuitively, involve breakthroughs in areas that have nothing to do with genomics, strictly speaking. Rather, biology may be on the brink of a golden age of theory, a time when scientists can rethink the basic conceptual structure of how phenotypes (individuals’ observable traits) are constructed from genotypes. And some of this disruption may not live in the biological realm at all, but instead, in fields like information theory and statistical physics.
Several examples suggest that this movement might already be underway. One such breakthrough, labeled the “omnigenic model,” transcends the established idea that traits are composed of multiple genetic parts or are “polygenic.” The omnigenic model turns the volume up, suggesting that many disease-related traits (and by extension, many traits of interest) are modulated by the indirect effect of mutations that can be scattered across the genome, rather than clustered within genes of interest. Like many other discoveries in recent times (including those involving RNA, awarded the Nobel Prize in physiology or medicine in 2023 and 2024), this model complexifies our picture of genetics and genomics: The quest for singular “jackpot” mutations that explain everything is a lost cause. We need to acknowledge that genetics is even more complex than we already know it to be.
Biology may be on the brink of a golden age of theory, a time when scientists can rethink the basic conceptual structure of how phenotypes are constructed from genotypes.
The post-genomics age has large implications for the sorts of science that might be produced in the future. Not only might theory find new terrain, but the many subfields of the biological sciences might move on from a scientific ecosystem driven by large laboratories, centralized power, and the importance of fraught notions like academic prestige. That is, because genomics breakthroughs over the past quarter-century required lots of hands, many groups who succeeded could rapidly accumulate talent and resources. Even if this accumulation was initially the product of a responsible meritocracy, it doubtlessly created a runaway Matthew effect, where groups who had acquired talent and resources had an easier time gathering more power and resources. While this is a challenge in many domains (not only scientific research), some suggest that this sort of status quo can come at the expense of innovation. We can aim for a world where the prizes in biology don’t require large-scale factory-science, but one where individuality, cross-disciplinarity, and creativity can thrive in our quest to solve the greatest existing questions in biology.
There are myriad reasons for excitement in this post-genomic era. The breakthroughs that will define this era are not solely about turning over old models of biology. Rather, this age contains brand new areas for discovery, many of which involve an appreciation for complexity in biological systems, which can add depth and beauty to our study of the natural world.
Nice article, but I think it overlooks a fundamental gap between the sequencing of the genome (the “genomic” era) and, say, gene-environment interactions (the “post-genomic” era). That fundamental gap is the function of each gene…what is the biological function of each gene. Genome sequencing merely gives us mostly knowledge about the order of the letters in genomic alphabet, some information about how they’re arranged, and little to no information on their function. We need to know their function before we can understanding gene-environment interactions, roles in cancer, etc etc. Let’s now skip this essential step…the International Mouse Phenotyping Consortium is doing just that.