Multitrait index based on factor analysis and ideotype‐design: proposal and application on elephant grass breeding for bioenergy

• Published in: Global change biology. Bioenergy Vol. 10; no. 1; pp. 52 - 60
• Main Authors: Rocha, João Romero do Amaral Santos de Carvalho, Machado, Juarez Campolina, Carneiro, Pedro Crescêncio Souza
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons, Inc 01-01-2018
• Subjects: biomass; BLUP; Breeding; Cenchrus purpureus; Cogeneration; Design factors; Elephant grass; energy co‐generation; Factor analysis; Genotypes; Grasses; multicollinearity; Napier grass; Pennisetum purpureum; Ranking; REML; Renewable energy; selection index; SEM; Studies
• ISSN: 1757-1693; 1757-1707
• DOI: 10.1111/gcbb.12443

This study proposes a new multitrait index based on factor analysis and ideotype‐design (FAI‐BLUP index), and validates its potential on the selection of elephant grass genotypes for energy cogeneration. Factor analysis was carried out, and afterwards, factorial scores of each ideotype were designed according to the desirable and undesirable factors, and the spatial probability was estimated based on genotype‐ideotype distance, enabling genotype ranking. In order to quantify the potential of the FAI‐BLUP index, genetic gains were predicted and compared with the Smith‐Hazel classical index. The FAI‐BLUP index allows ranking the genotypes based on multitrait, free from multicollinearity, and it does not require assigning weights, as in the case of the Smith‐Hazel classical index and its derived indices. Furthermore, the genetic correlation ‐ positive or negative ‐ within each factor was taken into account, preserving their traits relationship, and giving biological meaning to the ideotypes. The FAI‐BLUP index indicated the 15 elephant grass with the highest performance for conversion to bioenergy via combustion, and predicted balanced and desirable genetic gains for all traits. In addition, the FAI‐BLUP index predicted gains of approximately 62% of direct selection, simultaneously for all traits that are desired to be increased, and approximately 33% for traits which are desired to be decreased. The genotypes selected by the FAI‐BLUP index have potential to improve all traits simultaneously, while the Smith‐Hazel classical index predicted gains of 66% for traits that are desired to be increased, and −32% for traits that are desired to be decreased, and it does not have potential to improve all traits simultaneously. The FAI‐BLUP index provides an undoubtable selection process and can be used in any breeding programme aiming at selection based on multitrait.
Joining the traditional technique of factor analysis (Exploratory Factor Analysis) with the ideotype‐design (Confirmatory Factor Analysis) we were able to propose a multitrait index (FAI‐BLUP index) that takes into account the relationship between the traits and the final goal of the breeding programme (ideotype). The FAI‐BLUP index allowed ranking the elephant grass genotypes based on multitrait, free from multicollinearity and without assign weights as occur in the Smith‐Hazel classical index and its derived indexes. The FAI‐BLUP index indicated high performance genotypes of elephant grass for energy cogeneration, predicting equilibrate and superior genetic gains. Therefore, The FAI‐BLUP index is a technical advance tool finding interest and application in genetic breeding programmes.