Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding

Mol Plant. 2021 Jan 4;14(1):77-94. doi: 10.1016/j.molp.2020.12.012. Epub 2020 Dec 16.

Abstract

The functional genes underlying phenotypic variation and their interactions represent "genetic mysteries". Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences. Due to advances in high-throughput multi-omics technologies, we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research. In this article, we provide a brief overview of current strategies to explore genetic mysteries. We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources. Next, we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries. We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning, which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits, and also provide a detailed procedure for systematic dissection of genetic mysteries,. Finally, we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.

Keywords: crop breeding; genetic mystery; interactome big data; machine learning.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Big Data*
  • Crops, Agricultural / genetics*
  • Databases, Genetic
  • Genomics
  • Machine Learning
  • Plant Breeding*
  • Protein Interaction Mapping*