Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Abstract

Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

“…Meta-analyses of QTLs associated with a variety of traits have been recently conducted in different crops such as wheat ( Kumar et al, 2021 ; Kumar et al, 2022 A. C. ; Kumar et al, 2023 S. ; Saini et al, 2021 ; Saini et al, 2022 ; Tanin et al, 2022 ), rice ( Sandhu et al, 2021 ; Kumari et al, 2023 ), barley ( Akbari et al, 2022 ), common bean ( Shafi et al, 2022 ), pigeon pea ( Halladakeri et al, 2023 ), including maize ( Kaur et al, 2021 ; Sheoran et al, 2022 ; Wang et al, 2022 ; Gupta et al, 2023 ; Karnatam et al, 2023 ), for diverse traits, including both yield-related traits ( Semagn et al, 2013 ; Wang Y. et al, 2016 ; 2020 ; Chen et al, 2017 ; Zhou et al, 2020 ) and quality traits ( Jin et al, 2013 ; Dong et al, 2015 ). However, there is currently no comprehensive study on the genomic regions influencing both grain quality and yield in maize.…”

“…Meta-QTL analysis represents a conceptual approach that combines QTL discoveries from various studies and refines the positions of QTLs on a consensus map ( Goffinet and Gerber, 2000 ). When numerous QTLs associated with the specific trait are analysed together through meta-analysis, the resulting consensus QTLs are referred to as “meta-QTLs (MQTLs).” This approach can not only identify redundant QTLs and candidate genes (CGs), but also facilitate the selection of promising QTLs for breeding applications involving MAB ( Goffinet and Gerber, 2000 ; Sandhu et al, 2021 ; Saini et al, 2022 ; Sheoran et al, 2022 ; Tanin et al, 2022 ; Kumar A. et al, 2023 ; Halladakeri et al, 2023 ; Karnatam et al, 2023 ).…”

“…In maize, significant advancements have been made in the field of MQTL analysis, which involves identifying genetic regions associated with various traits such as fungal disease resistance ( Gupta et al, 2023 ), multiple abiotic stress tolerance ( Sheoran et al, 2022 ), popping traits ( Kaur et al, 2021 ), and root-related traits ( Karnatam et al, 2023 ). Additionally, specific MQTL studies have focused on yield-related traits ( Semagn et al, 2013 ; Wang Y. et al, 2016 ; Wang et al, 2020 ; Chen et al, 2017 ; Zhou et al, 2020 ) or quality-related traits ( Jin et al, 2013 ; Dong et al, 2015 ).…”