Genetic correlation, genome-wide association and genomic prediction of portable NIRS predicted carotenoids in cassava roots

Authors: IKEOGU, UGOCHUKWU - Cornell University; AKDEMIR, DENIZ - Cornell University; WOLFE, MARNIN - Cornell University; OKEKE, UCHE - Cornell University; CHINEDOZI, AMAEFULA - National Root Crops Research Institute (NRCRI); Jannink, Jean-Luc; EGESI, CHIEDOZIE - Cornell University

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/8/2019
Publication Date: 12/4/2019
Citation: Ikeogu, U.N., Akdemir, D., Wolfe, M.D., Okeke, U.G., Chinedozi, A., Jannink, J., Egesi, C.N. 2019. Genetic correlation, genome-wide association and genomic prediction of portable NIRS predicted carotenoids in cassava roots. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2019.01570.
DOI: https://doi.org/10.3389/fpls.2019.01570

Interpretive Summary: Cassava has white and yellow types, with yellow cassava containing vitamin A and eight other carotenoid compounds with vitamin A activity. We measured 173 cassava root samples for these carotenoids and for their reflectances through visible and near infrared spectra. We developed statistical models to predict provitamin A content from these spectra. The developed models were used to evaluate the carotenoids of 594 cassava clones with spectral information collected across three locations in a Nigerian national breeding program (NRCRI, Umudike). The predictions on the NRCRI population were used to assess the genetic correlations, identify DNA markers associated with carotenoids, and generate predictions based on markers. Estimates of genetic correlation showed various levels of the relationship among the carotenoids. We found markers significantly associated with carotenoids on chromosomes 1, 2, 4, 13, 14, and 15. One of the identified candidate genes, phytoene synthase, has been widely reported for variation in carotenoid content. On average, genomic prediction accuracies ranged from ~0.2 to 0.52. This study is one of the initial attempts in understanding the genetic basis of individual carotenoids.

Technical Abstract: Random forests (RF) was used to correlate spectral responses to known wet chemistry carotenoid concentrations including total carotenoid content (TCC), all-trans ß-carotene (ATBC), violaxanthin (VIO), lutein (LUT), 15-cis beta-carotene (15CBC), 13-cis beta-carotene (13CBC), alpha-carotene (AC), 9-cis beta-carotene (9CBC), and phytoene (PHY) from laboratory analysis of 173 cassava root samples in Columbia. The cross-validated correlations between the actual and estimated carotenoid values using RF ranged from 0.62 in PHY to 0.97 in ATBC. The developed models were used to evaluate the carotenoids of 594 cassava clones with spectral information collected across three locations in a national breeding program (NRCRI, Umudike), Nigeria. Both populations contained cassava clones characterized as white and yellow. The NRCRI evaluated phenotypes were used to assess the genetic correlations, conduct genome-wide association studies (GWAS), and genomic predictions. Estimates of genetic correlation showed various levels of the relationship among the carotenoids. The associations between TCC and the individual carotenoids were all significant (P < 0.001) with high positive values (r > 0.75, except in LUT and PHY where r < 0.3). The GWAS revealed significant genomic regions on chromosomes 1, 2, 4, 13, 14, and 15 associated with variation in at least one of the carotenoids. One of the identified candidate genes, phytoene synthase (PSY) has been widely reported for variation in TCC in cassava. On average, genomic prediction accuracies from the single-trait genomic best linear unbiased prediction (GBLUP) and RF as well as from a multiple-trait GBLUP model ranged from ~0.2 in LUT and PHY to 0.52 in TCC. The multiple-trait GBLUP model gave slightly higher accuracies than the single trait GBLUP and RF models. This study is one of the initial attempts in understanding the genetic basis of individual carotenoids and demonstrates the usefulness of NIRS in cassava improvement.