Inheritance of factors and validation of loci linked to the kernel row number in tropical field corn (Zea mays L.) Patil Sahana Police, Gadag R.N., Mukri Ganapati*, Singh Chandu, Bhat Jayant S., Gupta Navin C.1, Kumari Jyoti2, Kumari Shilpa, Chandra Prabha, Gowtham K.V. Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, 110 012, India 1ICAR-National Institute for Plant Biotechnology, New Delhi, 110 012, India 2ICAR-National Bureau of Plant Genetics Resources, New Delhi, 110 012, India *Corresponding Author: Ganapati Mukri, Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, 110 012, India, E-mail: ganapati4121@gmail.com
Online Published on 01 February, 2024. Abstract Sustainable feeding of a growing population with nutritional security in the era of climate change is the leading challenge facing a developing nation. Field corn is one of those crops that can help achieve this goal due to its high productivity and wide adaptation. There is scope for further improving field corn productivity by targeting component traits such as kernel row number (KRN). In the present investigation, the kernel row number displayed significant variation as well asa positive correlation with yield and yield component traits under the study. The inheritance of the KRN trait was analyzed using the Wright-Castle estimator and chi-square test in two sets of F2 populations (AH4499 and AH4500) and parental lines (AI 505, AI 541 and AI 542). The analyses by the Wright-Castle estimator revealed that KRN is governed by two effective factors (1.92@ 2) with four contributing alleles in the AH-4499 population and four effective factors (3.93 @ 4) with eight contributing alleles in the AH-4500 population. Further analysis by East’s hypothesis (frequency of recessive homozygote in F2=1/4n) produced similar results and the Chi-square test (0.01 level of significance) confirmed the non-significant difference between expected and observed recessive frequency in F2sof both the populations. This suggested that KRN is governed at least four genes with eight contributing alleles. In both the F2 populations, F1 value was non-significantly close to the mid-parent value suggesting the additive nature of KRN. Further, Bulked Segregant Analysis was carried out using AH-4500-F2 population having 231 individuals to validate linked loci. Out of 58 flanking SSR markers previously reported for the KRN trait, only nine markers were polymorphic for this population. These linked markers identified two putative QTLs for KRN i.e., qKRN2.1 and qKRN2.2 on chromosome 2 through inclusive composite interval mapping. The genetic distance with closely associated markers, bnlg 1017 was 9 cM for qKRN2.1 with a LOD score of 10.24 and a Proportion of Variance Explained (PVE%) of 16.86. The marker-trait association was further validated using F2:3 population and it was found that the marker bnlg 1017 showed a significant association with the KRN trait. Thus, the marker bnlg 1017 could be used to identify high KRN genotypes for use in breeding programs to enhance the productivity of tropical field corn. Top Keywords Inheritance, Kernel row number, Bulked segregant analysis, Composite interval mapping, Marker validation. Top |