Cassava, a root crop, is a critical staple food crop planted on 32 million acres worldwide. It is widely grown throughout sub-Saharan Africa, notably in Nigeria, DR Congo, Ghana, Angola, and Mozambique, but with large acreages in Vietnam, Brazil, Indonesia and India. Besides being a critical source of calories and dietary fiber for many poorer households throughout Africa it is also a very versatile crop, serving as an important source of animal feed and starch with uses in foods, glues, biodegradable products and drugs. It has a global market value of $48.7 billion and is also a priority crop for the Vision for Adapted Crops and Soils (VACS) program, led by the U.S. State Department, which aims to create resilient food systems in Africa by growing nutritious, climate-adapted crops in healthy soils.

Unlocking Cassava’s Potential for Food Security and Climate Resilience

Conventional breeding is a painstaking laborious process that takes many crop generations to develop the first in a stream of varieties that adapt to ever-evolving market and climate conditions. To speed up this process, the International Center for Tropical Agriculture (CIAT) sequenced thousands of cassava varieties to reveal genetic markers of adaptive and harmful traits. GEMS colleagues Nathan Carlson, Tom Kono, and Kevin Silverstein in the Minnesota Supercomputing Institute (MSI), developed a queryable genomics database to enhance cassava breeding and improvement efforts at CIAT and elsewhere. To do so they drew on the whole genome resequencing data spanning 3,673 accessions of cassava provided by CIAT and identified short DNA sequence variants among them–totalying over 9 million sequence variants! More specifically, the MSI team identified nonsynonymous variants, a subset of the sequence variants that change the amino acid sequence of the plants’ proteins from the reference genome sequence. The functional impact of the nonsynonymous variants was then predicted using a sequence constraint model called BAD_Mutations (Chun and Fay 2009, Kono et al. 2018) to identify sequence variants with potential impact on cassava trait variation.

cassava root

Tackling Deleterious Mutations

CIAT breeders, led by Sean Fenstemaker, are excited at the possibilities these data provide for them. Deleterious mutations can significantly reduce crop yield and quality. CIAT’s breeding program now incorporates BAD_Mutations, an innovative SNP annotation tool designed to identify harmful genetic variants in cassava. This tool employs a likelihood ratio test based on alignments of publicly available angiosperm genomes, allowing for improved detection of deleterious mutations. 

Why Use BAD_Mutations?

Jonathon Newby, Cassava Program Leader, CIAT noted that  “While smallholder cassava farmers are faced with a range of new threats, there are also many untapped opportunities for this formerly neglected crop to address food security and nutrition,and still be a globally competitive product in industrial and food application. The cassava variant database addresses challenges and explores new opportunities for cassava breeding to unlock this potential. By comparing genetic variants with their ancestral origins, it provides insights into diversity and traits conserved in plants, aiding in the identification of key genetic variations. The database also supports molecular marker development, parent selection, and breeding strategy refinement.”

Enhancing Breeding Strategies

BAD_Mutations helps identify and select against genetic variants that negatively impact traits of interest. By using this tool, breeders can enhance phenotypic variation, leading to the development of robust and high-yielding cassava varieties. This genomic precision is vital for adapting to changing environmental conditions and meeting market demands. Additionally, breeders may use BAD_Mutations as a strategy for in silico validation of trait-linked markers, further ensuring the accuracy and effectiveness of molecular breeding efforts.

CIAT is using BAD_Mutations in tandem with other advanced technologies such as flower-inducing and doubled haploid techniques. These methods, combined with the University of Minnesota’s genomic tools, facilitate backcrossing-based trait introgression and systematic exploration of heterosis, significantly improving breeding efficiency of CIAT and its partners.

While cassava genetics was the focus of this project, the resulting queryable database framework has much broader applications within agriculture. Identification of genetic variants of potentially large effect is a technique that is useful for general crop and animal improvement, especially for complex traits (e.g., yield) which are typically under the influence of many genetic loci. Construction of an efficient, query-ready database allows for the genetic variation data to be rapidly assessed with standard input and output formats, making it easier for researchers to interpret the data.

This project demonstrates how cross-institution collaborations can accelerate applied research efforts. By partnering, CIAT and UMN crunched through a very large set of genomics data (requiring continuous compute cycles on hundreds of supercomputer processors for a month) into a format that can easily be used by geneticists and breeders to improve an important staple crop.

Global Collaboration for Food Security

These advanced genomic tools are pivotal in enhancing crop resilience and productivity by addressing the challenge of deleterious genetic mutations. CIAT invites researchers and global partners to collaborate in using this new cassava variant effect database. There is a real urgency to accelerate crop breeding to address global food security and poverty reduction concerns in the face of consequential changes in climate worldwide. Novel partnerships that pool complementary resources are key to making significant strides that result in timely and climate-resilient improvements in cassava and other crops.
 

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