No 1

February 3, 2021

Wheat and peanuts are among the "big eight" foods listed by the United States Department of Agriculture that cause 90 percent of food allergies. Sachin Rustgi, a researcher at Clemson University and a member of the Crop Science Society of America (CSSA), and his colleagues are using plant breeding and genetic engineering to develop less allergenic varieties of wheat and peanuts.

For wheat, Rustgi's team is focused on gluten, a group of proteins that can cause an immune reaction for individuals with Celiac disease. Gluten genes are distributed all over a cell's DNA and it is difficult to develop wheat varieties with lower gluten levels. For peanuts, the team is working on the proteins that trigger allergic reactions. Like the gluten genes in wheat, peanut allergen genes are spread throughout the peanut DNA.

Rustgi and his team are now testing many varieties of wheat and peanuts to find the ones that are less allergenic. Through genetic engineering, the researchers work to reduce the allergenic proteins in the two crops. They are also using CRISPR to target the gluten genes in wheat. "Disrupting the gluten genes in wheat could yield wheat with significantly lower levels of gluten. A similar approach would work in peanuts," says Rustgi.

For more details, read the article in CSSA Science News.

 

No 2

Figure: Field trials showed successful use of gene editing to confer resistance to white mold in canola. 

White mold, also known as sclerotinia, is a fungal pathogen that can affect 14-30% of canola fields every year and can reduce yields by up to 50%. Thus, researchers at Cibus used their Rapid Trait Development System (RTDS) which involves gene editing without incorporating a foreign gene in the crop, thus retaining its non-GM status.

"Fundamentally, what we do is we make a spelling change in a gene and so by doing that we use the natural processes in cells of plants and then bring those cells back to a whole plant. We then take it to the greenhouse and it enters into a normal plant-breeding program," said Peter Beetham, President, and CEO at Cibus.

Combatting white mold has a couple of advantages, including the reduction of the carbon footprint. With fewer fungicide applications, less fuel is used by farmers. Resistance to the disease also ensures better yield and higher income for the farmers.

Read more from Cibus and the Western Producer.

Sikiru Adeniyi Atanda, Michael Olsen, Juan Burgueño, Jose Crossa, Daniel Dzidzienyo, Yoseph Beyene, Manje Gowda, Kate Dreher, Xuecai Zhang, Boddupalli M. Prasanna, Pangirayi Tongoona, Eric Yirenkyi Danquah, Gbadebo Olaoye & Kelly R. Robbins

Theoretical and Applied Genetics January 2021, vol. 134:279–294.

 

Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set.

The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

See: https://link.springer.com/article/10.1007/s00122-020-03696-9

 

 

Figure 2:  a Spectral decomposition of the genomic relationship matrix of DS1 (849 DH lines). The plot of the first two principal components shows the population structure in DS1, each dot represents a DH line and the colors are each bi-parental population. b Spectral decomposition of the genomic relationship matrix of DS2 (1389 DH lines). The plot of the first two principal components shows the population structure in DS2, each dot represents a DH line and the colors are each bi-parental population. c Spectral decomposition of the genomic relationship matrix of the combined D1 and DS2 (2238 DH lines). The plot of the first two principal components shows the interconnectedness across the datasets. Each dot represents a DH line, and the blue and red colors represent DH lines in DS1 and DS2, respectively