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Molecular Approaches for Enhancing Abiotic Stress Tolerance in Plants
Published in Hasanuzzaman Mirza, Nahar Kamrun, Fujita Masayuki, Oku Hirosuke, Tofazzal M. Islam, Approaches for Enhancing Abiotic Stress Tolerance in Plants, 2019
Sushma Mishra, Dipinte Gupta, Rajiv Ranjan
Several limitations associated with earlier methods of QTL mapping have been overcome by genome-wide association studies (GWAS), which are a method for high-resolution mapping of complex trait loci based on linkage disequilibrium. This method results in higher resolution (often up to gene level) by performing whole-genome scans to identify haplotype blocks that are significantly correlated with quantitative trait variation (Brachi et al., 2011). In addition, GWAS (also known as association mapping) is a quick and effective method that has been used in several crop species to identify candidate genes/QTLs involved in abiotic stress tolerance. This method is based on the screening of single nucleotide polymorphisms (SNPs) across different chromosomes of a tolerant variety and co-relating it with stress tolerance trait. In one of the reports, QTLs for salt tolerance in rice (during the reproductive stage) was identified by screening SNPs in a customized SNP array containing around 6000 stress-responsive genes (Kumar et al., 2015). This genotyping information was co-related with the phenotypic expression of 220 diverse rice accessions for several morphological, biochemical (Na+ and K+ content) and agronomic traits. Twelve of the twenty SNPs identified in this study were found in the region of Saltol, a major QTL on chromosome 1 in rice, involved in controlling salinity tolerance at seedling stage (Bonilla et al., 2002). Likewise, GWAS in 368 different rapeseed accessions revealed 75 SNPs distributed across 14 chromosomes, which were associated with four salt tolerance-related traits (Wan et al., 2017). The high throughput sequencing, combined with GWAS, has been used to identify potential molecular markers, such as SNPs, insertions and deletions, and copy number variations, which are associated with growth and development and/or stress responses.
Cadmium contamination in food crops: Risk assessment and control in smart age
Published in Critical Reviews in Environmental Science and Technology, 2023
Yan Huili, Zhang Hezifan, Hao Shuangnan, Wang Luyao, Xu Wenxiu, Ma Mi, Luo Yongming, He Zhenyan
Joint linkage association mapping (JLAM) and genome-wide association studies (GWAS) are the most common methods applied to excavate low-Cd related natural variations and quantitative trait loci (QTLs). JLAM is a method combining the advantages of linkage mapping and association mapping. In practice, the effects of QTLs measured by their linkage high-density molecular markers to target traits were calculated analyzed in a population. By using artificial mapping population, JLAM can effectively and accurately excavate QTLs and natural variations related to low-Cd accumulation (Würschum et al., 2012). To date, efforts in JLAM have yielded many successes, over 30 Cd accumulation related QTLs have been excavated in maize, rice and wheat (Table 1), including many important Cd transporters. For example, OsHMA3, a rice gene isolated using the Anjana Dhan and Nipponbare derived population, is located in the interval defined by RM21251 and RM21275 (Ueno et al., 2010). Using a double haploid (DH) rice population derived from TN1 and CJ0620, the defensin-like protein, CAL1, was identified (Luo et al., 2018). In maize and barley, ZmHMA3 was identified by fine mapping with bulked sergeant RNA-seq analysis using a biparental segregating maize population of Jing724 (low-Cd line) and Mo17 (high-Cd line) (Tang et al., 2021) and HvHMA3 was fine-mapped from a cross between BCS318 and Haruna Nijo (Lei et al., 2020). At present, populations used in natural low-Cd variation excavation are mainly double-parent populations such as DH (Luo et al., 2018), recombinant inbred lines (RIL) (Oladzad-Abbasabadi et al., 2018), chromosome segment substitution lines (CSSL) (Abe et al., 2013), in which limited genetic variants have been obtained. A more comprehensive excavation of natural low-Cd variation needs more complex artificial multiparent population like complete-diallel plus unbalanced breeding-derived inter-cross (CUBIC) (Liu, Wang, et al., 2020) population and nested associated mapping (NAM) population (Gage et al., 2020).