Abstract
Ionome contributes to maintain cell integrity and acts as cofactors for catalyzing regulatory pathways. Identifying ionome contributing genomic regions provides a practical framework to dissect the genetic architecture of ionomic traits for use in biofortification. Meta-QTL (MQTL) analysis is a robust method to discover stable genomic regions for traits regardless of the genetic background. This study used information of 483 QTLs for ionomic traits identified from 12 populations for MQTL analysis in Arabidopsis thaliana. The selected QTLs were projected onto the newly constructed genetic consensus map and 33 MQTLs distributed on A. thaliana chromosomes were identified. The average confidence interval (CI) of the drafted MQTLs was 1.30 cM, reduced eight folds from a mean CI of 10.88 cM for the original QTLs. Four MQTLs were considered as stable MQTLs over different genetic backgrounds and environments. In parallel to the gene density over the A. thaliana genome, the genomic distribution of MQTLs over the genetic and physical maps indicated the highest density at non- and sub-telomeric chromosomal regions, respectively. Several candidate genes identified in the MQTLs intervals were associated with ion transportation, tolerance, and homeostasis. The genomic context of the identified MQTLs suggested nine chromosomal regions for Zn, Mn, and Fe control. The QTLs for potassium (K) and phosphorus (P) were the most frequently co-located with Zn (78.3%), Mn (76.2%), and Fe (88.2% and 70.6%) QTLs. The current MQTL analysis demonstrates that meta-QTL analysis is cheaper than, and as informative as genome-wide association study (GWAS) in refining the known QTLs.
Highlights
Ionome contributes to maintain cell integrity and acts as cofactors for catalyzing regulatory pathways
Results of our meta-QTL analysis represent the coherence and consistency of MQTL analyses and genome-wide association study (GWAS) for the identification of genomic regions corresponding to the studied ionomic traits. This is the first MQTL analysis report that identified several major genomic regions associated with ionomic traits in A. thaliana
This analysis defines a genome-wide landscape on the most stable genomic regions (MQTL-3/Chr[2], MQTL-7/Chr[1], MQTL-1/Chr[3], and MQTL-3/Chr5) along with reliable genetic markers (CH.192L-Col, HH.357L, AD.191L-Col, SNP135, SNP105, degree of freedom (DF).252L, m291, and BH.127L) that provide a robust tool for breeding ionomic traits through marker-assisted selection
Summary
Ionome contributes to maintain cell integrity and acts as cofactors for catalyzing regulatory pathways. Meta-QTL (MQTL) analysis is a robust method to discover stable genomic regions for traits regardless of the genetic background. This study used information of 483 QTLs for ionomic traits identified from 12 populations for MQTL analysis in Arabidopsis thaliana. The information on QTLs derived from various population structure, origin and sizes can be used to refine the known QTLs regardless of the genetic backgrounds, marker density and phenotypic variations[21,24,25] In this way, a meta-QTL analysis helps to narrow down the confidence interval (CI) of the initial QTLs identified in independent populations and lays the foundation for a better understanding of traits underlying a QTL region than what is possible in independent QTL mapping s tudies[26]. The MQTL analysis helps to validate the genetic association of loci identified by genome-wide association study (GWAS) a pproach[27,28,29]
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