Joint analysis of phenotypic and molecular data for genetic diversity assessment in extra-early orange maize (Zea Mays L.).

Authors

Tégawendé Odette Bonkoungou, Idris Ishola Adejumobi, Victor Olawale Adetimirin, Baffour Badu-Apraku, Paterne Angelot Agre, Kiswendsida Romaric Nanema, Adamu Masari Abubakar, Abdalla Dao, Wende Mengesha

Published in

BMC genomics. Volume 26. Issue 1. Pages 784. Aug 28, 2025. Epub Aug 28, 2025.

Abstract

Maize is one of the most important cereals in the world. To maximize the potential of hybrids from heterosis, knowledge of genetic diversity is crucial. This study aimed to examine the population structure and genetic diversity of new extra-early elite orange maize inbred lines of the IITA-Maize Improvement Program using phenotypic and genome-wide SNP markers. One hundred and eighty-seven orange inbred lines sourced from four populations were phenotyped under well-watered conditions and genotyped using the high-density DArTseq platform.
Examination of the genetic dissimilarity heatmap based on the Gower matrix derived from phenotypic data showed low variability among the inbred lines, while moderate variability existed based on the identical-by-state (IBS) matrix from SNP markers. Gower matrix (Phenotypic data) assigned the 187 inbred lines into two distinct groups, while IBS matrix (SNP marker data) assigned the inbred lines into four groups. The cophenetic correlation between the two genetics groups (Gower and IBS) was low, indicating a lack of concordance. A joint matrix derived from the Gower and IBS matrices assigned the 187 inbred lines into three groups. Mantel correlation of the combined matrix showed 0.81 and 0.68 magnitudes with the Gower and IBS matrix, respectively.
The outcome of this study provided new insights into the genetic diversity and population structure of newly developed extra-early orange inbred lines. By facilitating the optimal use of heterosis in hybrid maize breeding, these findings will contribute to the development of high-yield potential and provitamin A-rich varieties, ultimately improving food security and nutrition in the region.

PMID:
40877794
Bibliographic data and abstract were imported from PubMed on 29 Aug 2025.

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