Study explores the promises and pitfalls of evolutionary genomics - Math News

Study explores the promises and pitfalls of evolutionary genomics – Math News

The 2nd-century Alexandrian astronomer and mathematician Claudius Ptolemy had a grand ambition. Hoping to make sense of the motion of the stars and the paths of the 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 movements of the celestial objects that he observed.

Unfortunately, a fatal flaw 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. The Ptolemaic universe, made up of complex “epicycles” to account for the movements of the planets and stars, has long been recorded in the history books, though its conclusions have remained scientific dogma for more than 1,200 years.

However, the field of evolutionary biology is prone to flawed theoretical approaches, sometimes producing impressive models that nonetheless fail to convey the true workings of nature shaping the dizzying array of life forms on Earth.

A new study examines mathematical models designed to draw conclusions about how evolution works at the level of populations of organisms. The study concludes that such models must be built very carefully, avoiding unwarranted initial assumptions, weighing the quality of existing knowledge, and remaining open to alternative explanations.

If strict procedures are not applied in the construction of null models, theories may emerge that seem to agree with some aspects of the available data derived from DNA sequencing, but that do not adequately elucidate the underlying evolutionary processes, which are often very complex and multifaceted.

Such theoretical frameworks can offer compelling but ultimately flawed pictures of how evolution actually affects populations over time, whether they be populations of bacteria, schools of fish, or human societies and their various migrations during prehistory.

In the new study, Jeffrey Jensen, a researcher at the Arizona State University Biodesign Center for Mechanisms of Evolution and a professor in the Center for Evolution and Medicine’s College of Life Sciences, leads a group of international luminaries in the field who provide advice for future research. Together they describe a variety of criteria that can be used to better ensure the accuracy of models that produce statistical inferences in population genomics, a scientific discipline concerned with large-scale comparisons of DNA sequences within and between populations and species.

“One of our key messages is the importance of considering the contributions of evolutionary processes that are likely to be in constant operation (such as purifying selection and genetic drift), before simply relying on hypothetical or rare ones as the main drivers of the observed variation of population (such as positive population variation. selection),” Jensen noted.

The results of the research appear in the current issue of the journal. PLOS BIOLOGY.

a ripe field

Population genomics arose when early efforts in the field attempted to reconcile Charles Darwin’s notion of evolution through natural selection with early insights into the mechanisms of inheritance discovered by the Augustinian monk Gregor Mendel.

The synthesis culminated in the 1920s and early 1930s, largely due to the mathematical work of Fisher, Haldane, and Wright, who pioneered the exploration of how natural selection, along with other evolutionary forces, would alter the composition genetics of Mendelian populations over time.

Today, population genomics studies involve the large-scale application of various genomic technologies to explore the genetic makeup of biological populations and how various factors, including natural selection and genetic drift, produce changes in genetic makeup with time.

To do this, population geneticists develop mathematical models that quantify the contributions of these evolutionary processes in the formation of gene frequencies, and 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 your conclusions. against the accumulated data. .

the spice of life

The study of genomic variation focuses on DNA sequence differences between individuals and populations. Some of these variants are critically important for 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 common source of variation is known as single nucleotide polymorphisms, or SNPs, where a single DNA letter in the genome is changed. But larger-scale variation of the genome, involving the simultaneous alteration of hundreds or even thousands of base pairs, is also possible. Again, some of these alterations 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 have differential fitness with each other. By designing and studying mathematical models that govern the frequency change of corresponding genes and applying these models to empirical data, population geneticists seek to understand contributing evolutionary processes in a rigorous and quantitative manner. Therefore, population genetics is often considered the theoretical cornerstone of modern Darwinian evolution.

Drifting through the genome

Although the importance of natural selection to the evolutionary process is undeniable, the role of positive selection in increasing the frequency of beneficial variants, the potential driver of adaptation, is relatively rare compared to even other forms of natural selection. For example, purifying selection (the removal of deleterious variants from the population) is a constantly acting and much more generalized form of selection.

In addition, there are multiple non-selective evolutionary processes of great importance. For example, genetic drift describes the many stochastic fluctuations inherent in evolution. In large populations, natural selection can act more effectively in eliminating harmful variation and potentially fixing beneficial variation, whereas as populations get smaller, genetic drift will become more and more dominant.

The distinction can be seen in a dramatic way when prokaryotic organisms such as bacteria are compared with organisms composed of eukaryotic cells, including humans. In the first case, large population sizes tend to result in more efficient selection. In contrast, a weaker selection pressure operating in eukaryotes is more permissive for genomic modifications, as long as they are not very detrimental.

According to the neutral theory of molecular evolution, a now guiding principle of the evolutionary theory proposed by population geneticist Motoo Kimura more than 50 years ago, most evolutionary change at the molecular level in real populations is not governed by the natural selection, but by genetics. . derivative. The study underlines that evolutionary biologists often miss this critical point. As co-author Michael Lynch, director of ASU’s Biodesign Center for Mechanisms in Evolution, observes, “Natural selection is just one of many evolutionary mechanisms, and not realizing it is probably the most significant obstacle to successful integration of theory.” of evolution”. with molecular, cellular and developmental biology.

The new consensus study further highlights that failing to consider these alternative evolutionary mechanisms that are sure to work, including genetic drift, and incorporate them into population genomics models is likely to mislead researchers. Excessive reliance on purely adaptive models to explain genomic variation has led to a number of interpretations of questionable value, the authors argue.

The study presents a detailed flowchart that can help guide the development of more precise 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 vital role in determining the molecular evolution and variation observed.

“While these many considerations may seem overwhelming to some researchers, it is important to note that many excellent research groups at ASU and around the world are actively advancing our understanding of these underlying evolutionary parameters, providing ever-improving inference, for example, of mutation and recombination rates,” added co-author Susanne Pfeifer, an assistant professor in the Center for Evolution and Medicine and the Biodesign Center for Mechanisms of Evolution.

Where theoretical models of population genomics once proliferated alongside relatively sparse genomic data, today an avalanche of data, made possible by rapid and inexpensive DNA sequencing of organisms throughout the tree of life, has radically changed the field. Careful and judicious use of this goldmine of genomic data will help advance more rigorous models to unravel the many remaining mysteries of evolution.

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