Genetic mapping of tolerance to iron deficiency chlorosis in peanut (Arachis hypogaea L.)

Abstract

Iron de ciency chlorosis (IDC) under calcareous and alkaline soils is a signi cant abiotic stress affecting the growth and yield of peanut. In this study, the genomic regions governing IDC tolerance were mapped using a recombinant inbred line (RIL) population derived from TMV 2 (susceptible to IDC) and TMV 2-NLM (tolerant to IDC), which was phenotyped during the rainy seasons of 2019 and 2020 in the iron-de cient calcareous plots. The best linear unbiased prediction (BLUP) values for IDC tolerance traits like visual chlorotic rating (VCR), and SPAD chlorophyll meter reading (SCMR) were used for QTL analysis along with a genetic map carrying 700 GBS-derived SNP, AhTE and SSR markers. In total, 11 and 12 maineffect QTLs were identi ed for VCR and SCMR, respectively. Among them three QTLs were major with the phenotypic variance explained (PVE) of 10.3-34.4% for VCR, and two QTL were major for SCMR with 3 cM) on chromosome Ah13 carrying two QTLs (one each for VCR and SCMR) was consistent with the previous report. A SNP marker, Ah14_138037990 identi ed from single marker analysis for VCR was located in the intronic region of the gene Arahy.QA0C1, which is important for protein-binding. Overall, this study identi ed new QTLs and also validated QTL for IDC tolerance. These genomic resources could be useful for genomics-assisted breeding of peanut for IDC tolerance.

Genetic mapping of drought tolerance traits phenotyped under varying drought stress environments in peanut (Arachis hypogaea L.)

“…Overall, it was observed that the stress condition could differentiate the RILs much better than the non-stress condition for the physiological traits like SLA_70 and its inverse trait SDW_70. Eight markers (AhTE0281, AhTE0087, AhTE0275, AhTE0120, AhTE1110, AhTE0242, AhTE0060 and Ah003_127278448) associated with SDW_70 in this study were also associated with protein content (Jadhav et al 2021) and tolerance to iron deficiency chlorosis (Tayade et al 2022) in peanut in the previous studies.…”

“…Two major effect QTLs on Ah12 (0.0-5.7 cM) and Ah16 (0.0-7.1 cM) governed SLA_70 and SDW_70 through their main effects and epistatic interactions with a genomic region on Ah12 (73.5 cM). The region on Ah04 (0.0-19.7 cM) carrying two co-localized QTLs qPYP-PRD-Ah04 and qSLA70PRT-Ah04/qSDW70PRT-Ah04 was also previously reported to govern drought tolerance traits like transpiration, total dry matter and SCMR (Varshney et al 2009;Ravi et al 2011). Another genomic region (106.2-118.7 cM) on Ah05 carrying two colocalized major QTLs for PYPP and SDW_70 was also previously reported to govern transpiration efficiency, total dry matter and shoot dry weight by Gautami et al (2012).…”

“…This study reports mapping of QTLs and markers for drought tolerance in peanut by evaluating a RIL population derived from TMV 2 and its mutant TMV 2-NLM in six seasons over two locations, comprising of both normal and drought stress conditions. Previously, this mapping population has been used for mapping the taxonomic traits (Hake et al 2017), quality traits (Jadhav et al 2021) and iron deficiency tolerant traits (Tayade et al 2022), and this success could mainly be attributed for the small genomic region differing between the two parents (TMV 2 and its own mutant), and also due to the availability of a fairly dense genetic map. TMV 2 and TMV 2-NLM also differed for a few drought tolerance and productivity traits.…”

“…Of the three (qPYPPRD-Ah02, qPYPPRD-Ah04 and qPYPPRD-Ah05) major main effect QTLs for PYPP, the first two were involved in the epistatic interactions, and the last two were previously reported to govern tolerance to iron deficiency chlorosis (Tayade et al 2022), and number of pods per plant and oil content (Jadhav et al 2021). Since the favourable allele at all the three loci were contributed by TMV 2-NLM which is generally low yielding than TMV 2, the importance of other genomic regions contributing for PYPP could be expected.…”

“…In the past few years, considerable efforts have been made to map drought tolerance traits using genome-wide markers in peanut (Manjonda et al 2018). Identification of QTLs for transpiration efficiency, SCMR, shoot dry weight, leaf area, pod yield per plant (PYPP) were reported (Varshney et al 2009;Ravi et al 2011;Gautami et al 2012) though with low-density maps. Genetic mapping of drought tolerance using a dense map was recently reported by Pandey et al (2021).…”