Deploying Big Data to Crack the Genotype to Phenotype Code

Abstract

Mechanistically connecting genotypes to phenotypes is a longstanding and central mission of biology. Deciphering these connections will unite questions and datasets across all scales from molecules to ecosystems. Although high-throughput sequencing has provided a rich platform on which to launch this effort, tools for deciphering mechanisms further along the genome to phenome pipeline remain limited. Machine learning approaches and other emerging computational tools hold the promise of augmenting human efforts to overcome these obstacles. This vision paper is the result of a Reintegrating Biology Workshop, bringing together the perspectives of integrative and comparative biologists to survey challenges and opportunities in cracking the genotype to phenotype code and thereby generating predictive frameworks across biological scales. Key recommendations include: promoting the development of minimum “best practices” for the experimental design and collection of data; fostering sustained and long-term data repositories; promoting programs that recruit, train, and retain a diversity of talent and providing funding to effectively support these highly cross-disciplinary efforts. We follow this discussion by highlighting a few specific transformative research opportunities that will be advanced by these efforts.

Punnett Squares or Protein Production? The Expert–Novice Divide for Conceptions of Genes and Gene Expression

“…In other words, students should be able to explain the underlying causes of natural phenomena with a mechanistic model ( Braaten and Windschitl, 2011 ). Westerman and colleagues (2020 p. 385) posit that “mechanistically connecting genotypes to phenotypes is a longstanding and central mission to biology.” We define mechanistic reasoning similarly to Abrams and Southerland (2001) , as the ability to answer a “How does it occur?” question with a “how” and not a “because” answer. Studies show that students often focus on the end result (the outcome) while missing the process or processes (the mechanism, or the “how”) that drive the outcome ( Abrams and Southerland, 2001 ; Trujillo et al.…”