Identification of QTLS for yield and contributing traits in maize-teosinte derived bils under diseased-stressed and control conditions

Abstract

In maize, grain yield is the most important trait having a complex inheritance pattern. Yield contributing traits are more stable and have higher heritability than yield. Therefore, the present study was conducted to identify quantitative trait loci (QTLs) associated with grain yield and its components by using simple sequence repeat (SSR) markers. A population of 169 BC1F5 lines was derived from the crossing between maize inbred line DI-103 and teosinte-parviglumis was utilized for genotyping and phenotyping. In diseased stressed condition (E1), ear length (EL), ear diameter (ED), kernel rows per ear (KR/E), kernels per row (K/R), test weight (TW), and grain yield per plant (GY/P) had 7, 6, 7, 4, 6 and 5 QTLs whereas, in controlled condition (E2) 5, 2, 5, 4, 5 and 3 QTLs were detected for enlisted characters, respectively. Consistent QTLs across the environments were detected for 5 of the 6 investigated traits and number of QTLs were EL (2), ED (1), KR/E (3), TW (1), and GY/P (1) whereas, for K/R none of the QTLs were common between E1 and E2. By mapping analysis, we have identified genomic regions associated with two traits in a manner that was consistent with phenotypic correlations among traits, supporting either pleiotropy or tight linkage among QTLs. Three co-localized QTLs were identified between grain yield and contributing traits. Notably umc1720-linked QTL at bin 4.10 was simultaneously responsible for GY and EL, ED, KR/E, K/R; umc1215-linked QTL at bin 6.03 was simultaneously responsible for GY and ED, KR/E, K/R, TW; umc1279-linked QTL was responsible for GY and ED, TW. The findings suggest that the chromosomal region containing co-localized QTLs governing multiple yields associated traits are potential targets for selection. In addition for 6 studied traits, 44 superior lines were identified, and along with both the parents i.e. maize (DI-103) and teosinte they were clustered in 11 groups. Therefore, lines clustered independently can be utilized in a hybridization programme for the accumulation of yield contributing traits for yield maximization.