The 2nd-century Alexandrian astronomer and mathematician Claudius Ptolemy had big ambitions. Hoping to make sense of the movement of stars and the orbits of planets, he published a masterful treatise on the subject known as the Almagest. Ptolemy created a complex mathematical model of the universe that seemed to recapitulate the motions of celestial objects he observed.
Unfortunately, a fatal error was at the heart of his cosmic plan. Following the prejudices of his time, Ptolemy assumed that the earth was the center of the universe. Composed of complex “epicycles” to explain the movements of planets and stars, the Ptolemaic universe has long been recorded in the history books, although its conclusions have remained scientific dogma for over 1200 years.
The field of evolutionary biology is nonetheless prone to flawed theoretical approaches, sometimes producing impressive models that fail to convey the true workings of nature that shapes the dizzying diversity of living things on earth.
A new study examines mathematical models developed to draw inferences about how evolution works at the population level of organisms. The study concludes that such models must be constructed with great care, avoiding unjustified initial assumptions, weighing the quality of existing knowledge, and remaining open to alternative explanations.
Failure to apply rigorous procedures in the construction of null models can lead to theories that appear to agree with some aspects of the available DNA sequencing data, but do not properly elucidate the underlying evolutionary processes, which are often very complex and multifaceted.
Such theoretical frameworks can provide compelling, but ultimately misleading, pictures of how evolution actually affects populations over time, whether they are bacterial populations, schools of fish, or human societies and their various prehistoric migrations.
In the new study, Jeffrey Jensen, a researcher at Arizona State University’s Biodesign Center for Mechanisms of Evolution and a professor in the School of Life Sciences at the Center for Evolution & Medicine, leads a group of international luminaries in the field in advising on futures research. Together, they describe a set of criteria that can be used to better ensure the accuracy of models that generate statistical inference in population genomics — a scientific discipline dealing with large-scale comparisons of DNA sequences within and between populations, populations, and populations types concerned.
“One of our key messages is the importance of considering the contributions of evolutionary processes that are sure to be constantly in operation (like purifying selection and genetic drift) before simply relying on hypothetical or rare ones as primary drivers of the observed population variation (like positive selection)” , emphasized Jensen.
The research results appear in the current issue of the journal PLOS BIOLOGY.
A field matures
Population genomics arose when early efforts in the field attempted to reconcile Charles Darwin’s idea of evolution through natural selection with early insights into the mechanisms of inheritance discovered by the Augustinian monk Gregor Mendel.
The synthesis peaked in the 1920s and early 1930s, largely due to the mathematical work of Fisher, Haldane, and Wright, who pioneered the study of how natural selection, along with other evolutionary forces, shaped the genetic makeup of Mendelian populations in the would change over time.
Today, population genomics studies involve the large-scale application of various genomic technologies to study the genetic composition of biological populations and how various factors, including natural selection and genetic drift, produce changes in the genetic makeup over time.
To this end, population geneticists develop mathematical models that quantify the contributions of these evolutionary processes to the formation of gene frequencies, use this theory to design statistical inference approaches to estimate the forces that produce the observed patterns of genetic variation in real populations, and test their conclusions against collected data. .
The spice of life
The study of genomic variation focuses on DNA sequence differences between individuals and populations. Some of these variants are critical to biological function, including mutations responsible for genetic diseases, while others have no detectable biological effect.
Such variation in the human genome can take many forms. A frequent source of variation are so-called single nucleotide polymorphisms or SNPs, in which a single DNA letter in the genome is changed. Larger-scale variation of the genome, changing hundreds or even thousands of base pairs simultaneously, is also possible. Again, some of these changes may play a role in disease risk and survival, while many others have no effect.
Natural selection can occur when different segregating variants in a population exhibit a fitness difference from one another. By developing and studying mathematical models that control the change in abundance of corresponding genes, and applying these models to empirical data, population geneticists attempt to understand the contributing evolutionary processes in a rigorous and quantitative manner. Therefore, population genetics is often viewed as the theoretical cornerstone of modern Darwinian evolution.
drifting through the genome
While the importance of natural selection to the evolutionary process is undeniable, the role of positive selection in increasing the abundance of advantageous variants—the potential driver of adaptation—is certainly relatively rare compared to other forms of natural selection. For example, purifying selection – the elimination of harmful variants from the population – is an ongoing and much more pervasive form of selection.
In addition, there are several non-selective evolutionary processes of great importance. For example, gene drift describes the many stochastic fluctuations inherent in evolution. In large populations, natural selection can work more effectively, eliminating harmful variations and eliminating potentially beneficial variations, while genetic drift becomes increasingly dominant as populations get smaller.
The distinction can be seen in dramatic form when comparing prokaryotic organisms such as bacteria to organisms made up of eukaryotic cells, including humans. In the former case, the large population sizes tend to lead to more efficient selection. In contrast, weaker selection pressures operating in eukaryotes are more tolerant of genomic modifications provided they are not highly deleterious.
According to the neutral theory of molecular evolution—a guiding tenet of evolutionary theory proposed today by population geneticist Motoo Kimura more than 50 years ago—most evolutionary changes at the molecular level in real populations are determined not by natural selection but by genetics. Derivative. The study underlines that this critical point is all too often overlooked by evolutionary biologists. As co-author Michael Lynch, director of ASU’s Biodesign Center for Mechanisms in Evolution, notes, “Natural selection is just one of many evolutionary mechanisms, and the failure to recognize it is probably the greatest obstacle to successfully integrating evolutionary theory with Molecular, Cellular and Developmental Biology.
The new consensus study further highlights that failure to consider these alternative evolutionary mechanisms that certainly work, including genetic drift, and incorporate them into population genomics models will likely lead to misguided researchers. The over-reliance on purely adaptive models to explain genomic variation has led to a variety of interpretations of questionable value, the authors argue.
The study presents a detailed flowchart that can aid in the development of more accurate models used to draw evolutionary inferences based on genomic data. Biological parameters that vary between species include not only evolutionary variables such as population size, mutation rates, recombination rates, and population structure and history, but also how the genome itself is structured and life history traits, including mating behavior. All of these factors play a crucial role in determining the observed molecular variation and evolution.
“While these many considerations may seem daunting to some researchers, it is important to note that many excellent research groups at ASU and around the world are actively improving our understanding of these underlying evolutionary parameters by providing constant refinement of inference, for example from Mutations and recombination rates,” adds co-author Susanne Pfeifer, assistant professor at the Center for Evolution & Medicine and the Biodesign Center for Mechanisms of Evolution.
Where once theoretical models of population genomics proliferated alongside relatively sparse genomic data, today an avalanche of data made possible by the rapid and inexpensive DNA sequencing of organisms across the tree of life has radically changed the field. Careful and judicious use of this goldmine of genomic data will help advance the most rigorous models to unravel the many remaining mysteries of evolution.
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