Tools for using the International Rice Genebank to breed for climate-resilient varieties

Traditional rice varieties have been critical for developing improved stress-tolerant rice varieties. Tools to analyze the genome sequences of traditional varieties are accelerating the identification and deployment of genes conferring climate-change resilience.

Traditional varieties have served as critical sources of stress tolerance that can be deployed through crop breeding to improve varieties that are stress-sensitive but high-yielding under favorable conditions. Examples of such “upgrading” of existing varieties to improve their adaptation to changing climate conditions include sub1 varieties (for submergence tolerance) and DRR42 (for drought tolerance).

Important resources enabling the exploitation of traditional varieties are the genetic stocks (which have been purified from the traditional variety by single seed descent to ensure they are not mixed with other varieties), their genomic data, and tools that perform genetic analysis to identify potential stress-tolerant donors and favorable haplotypes.

An important set of genetic stocks used extensively by the global community is the diverse set of 3,000 rice genomes (3K RG) originating from 89 countries, all of which have been sequenced.

This detailed 3K RG analyses has had 89k accesses and 699 Web of Science citations, indicating the work is of broad interest. The SNP-Seek database provides access to the 3K RGsequence data and provides tools facilitating analysis for gene discovery of climate change–related traits, nutrition, yield, and other traits.

Since 2014, SNP-Seek has served 212,581 sessions from 163 countries, with an average of 50 sessions per day (via Google Analytics). Through SNP-Seek, users can further explore identified genomic regions for given traits to help pinpoint genes and markers that can be used in breeding, for example, by obtaining variant matrices, exploring annotations of regions (including alignments and variants), constructing haplotypes that define ne the patterns, and supporting candidate genes in peaks after association analysis by adding evidence.