Mendel's and Wallace's 200 Year Anniversary–Modern Synthesis in the Genomics Era

Dear Colleagues,

The years 2022 and 2023, respectively, mark Gregor Mendel's and Alfred Russel Wallace's 200-year anniversaries. Interestingly, the first centennial commemoration matched the “modern synthesis”, which refers to the early 20th century convergence of classical Wallace/Darwin selection theory with the allelic-based Mendelian genetics to explain the origin of biodiversity. Despite these efforts, each paradigm led to somehow different interpretations of the underlying genetic bases of traits under selection: the variance-focused Fisherian polygenetic conception on the one hand, and on the other hand the more deterministic single gene with major effects scenario. Separate sub-disciplines, the quantitative genetics and the molecular biology fields, ultimately embraced each paradigm. Despite quantitative genetics and genomics having better converged in recent decades, this interplay has not always been bidirectional. For example, molecular markers have largely served as tools to test the quantitative genetics hypothesis, yet until more recently, quantitative genetics theory has provided the framework to map underlying genes and molecular networks and predict their phenotypic consequences via modern polygenic risk scores and genomic prediction models. These latter innovations have partly been possible thanks to better sequencing and computer power. Consequently, it is expected and necessary that quantitative genetics and molecular biology continue converging, for instance, throughout the omics, big data and machine learning paradigms. Therefore, this Special Issue aims at presenting an overview of the 21st century “modern synthesis”, as a celebration of Mendel's and Wallace's 200-year anniversary, by compiling innovative trans-disciplinary research and methodologies across diverse study systems with the common denominator of pushing the integration of the fields of quantitative genetics and molecular biology via omics, modeling and big data tools.

Dr. Andrés J. Cortés
Guest Editor

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• biodiversity
• quantitative genetics
• molecular genetics
• comparative genomics
• modeling
• big data
• omics
• genomic prediction
• machine learning