Abstract
In 1978, Paolo Menozzi, Alberto Piazza, and Luca Cavalli-Sforza paved the ground for a new multidisciplinary approach to the study of human prehistory, interpreting genetic evidence in the light of archaeological information. By producing synthetic maps of allele frequencies and summarizing them by principal component analysis (PCA), they identified an association between patterns in genetic diversity across Europe and in the Neolithic archaeological record showing the earliest documented dates of farming societies. Based on this observation, they proposed a model of demic diffusion from the Near East. They argued that the observed patterns were the result of population growth due to increased food availability in early farming communities, westward dispersal of early farmers, and relative isolation between dispersing farmers and local hunter-gatherers. These results played a major role in our understanding of the Neolithic transition, but were also criticized on methodological grounds. For instance, it has become increasingly clear that the interpretation of PCA plots is less straightforward than originally thought, and correlations should be corroborated by explicit comparison of alternative demographic models. Despite these valid criticisms, genetic and genomic studies, including those involving ancient DNA, have largely confirmed the crucial role of the Neolithic transition as a process of demographic change in European prehistory, with some qualifications. Today, there is still much to be learned about the details of that complex history, but many researchers regard the European population structure as largely reflecting the genetic consequences of three major migrations: from Africa in Upper Paleolithic times, from the Near East at the beginning of the Neolithic, and from the eastern steppes in the Bronze Age. This deep structure has not been erased, despite many additional processes involving historical migrations, isolation (i.e., drift) and local gene flow, and has been recognized thanks to the pioneering work of Menozzi, Piazza and Cavalli-Sforza. Based on “Menozzi P, Piazza A, Cavalli-Sforza LL Synthetic maps of human gene frequencies in Europeans. Science 1978;201:786-792.”
Highlights
Already in the 1930s, through what Julian Huxley called the modern synthesis, i.e. the development of models describing the transmission of quantitative traits, population genetics had become a fundamental tool in the study of evolution
The word genomics did not exist at that time, but in the 1960s Luca Cavalli-Sforza was already thinking in genomic terms; he was the first to propose comparisons of multiple genes to infer the relative role of selection and drift in causing variation [2]; see Richard Nichols’ contribution in this issue
Luca Cavalli-Sforza’s intuition told him that, if he could somehow overcome these difficulties, he could exploit the full potential of population-genetic methods and obtain new insights into human evolutionary history
Summary
In their 1978 Science paper, Paolo Menozzi, Alberto Piazza and Luca Cavalli-Sforza [1] showed how we could read in our cells the message sent to us by millions of ancestors, finding a way to disclose otherwise obscure aspects of our past. Already in the 1930s, through what Julian Huxley called the modern synthesis, i.e. the development of models describing the transmission of quantitative traits (as well as the effects of inbreeding, selection, gene flow and drift), population genetics had become a fundamental tool in the study of evolution. The word genomics did not exist at that time, but in the 1960s Luca Cavalli-Sforza was already thinking in genomic terms; he was the first to propose comparisons of multiple genes to infer the relative role of selection and drift in causing variation [2]; see Richard Nichols’ contribution in this issue. The Menozzi et al study [1] is recognized as the starting point of a deeper investigation of the human past, based on an unprecedented dialogue among experts of diverse fields, such as paleontology, archaeology, anthropology, linguistics and, population genetics
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