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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.
Glossary of scientific and technical terms in bioengineering and biological engineering
Published in Megh R. Goyal, Scientific and Technical Terms in Bioengineering and Biological Engineering, 2018
Quantitative trait loci (QTLs) refer to genetic loci that affect phenotypic variation (and potentially fitness), which are identified by a statistically significant association between genetic markers and measurable phenotypes. Quantitative traits are often influenced by multiple loci as well as environmental factors.
Biotechnology for Drought Improvement
Published in Saeid Eslamian, Faezeh Eslamian, Handbook of Drought and Water Scarcity, 2017
Danial Kahrizi, Kasra Esfehani, Ali Ashraf Mehrabi, Matin Ghaheri, Zahra Azizi Aram, Solmaz Khosravi, Saeid Eslamian
Because of the difference in the genetic background and environment, such QTL may not provide a consistent effect. Furthermore, the QTL may not be transferable to other backgrounds because of negative epistatic interactions resulting in reduced or no effects in the new genetic background [16].
Posthumanism: Creation of ‘New Men’ Through Technological Innovation
Published in The New Bioethics, 2021
Two important difficulties in correlating genes with traits are: (i) the association of more than one gene with a particular trait, and (ii) the association of a gene with more than one trait. The polygenicity of most human traits is only one of the problems encountered when considering their modification by the manipulation of genes. Multiple gene inheritance refers to a group of genes that interact collectively to influence a phenotypic trait. These genes are referred to as a quantitative trait locus because the traits they affect correspond to quantitative characters whose phenotypes vary continuously and not in discretely identifiable types. An important property of these genes is that their individual effects are usually relatively small and interchangeable such that identical phenotypes may be displayed by a great variety of genotypes. In addition, the phenotypic expression of polygenic characters can be considerably modified by environmental influences.
Prospect of phytoaccumulation of arsenic by Brassica juncea (L.) in Bangladesh
Published in International Journal of Phytoremediation, 2018
Moupia Rahman, Md. Jakariya, Nazmul Haq, Mohammad Amirul Islam
Varying level of arsenic contamination in groundwater and soil is reported (Chowdhury et al. 2017; BGS 2000; Map 1). However, severity of the contamination is not the same in different districts. Our national data suggests that despite arsenic contamination, Bangladeshi farmers grow mustard and rape seed in almost all of the districts (Map 2). This widens the scope of phytoaccumulation of arsenic by B. juncea (rai and BARI 11). An earlier study by Rahman et al. (2012) suggests that there is a genetic effect of phytoaccumulation of arsenic by B. juncea (BARI 11 and Rai) and stem diameter is a morphological marker. Hence, there is a potential to enhance the uptake behavior of B. juncea using breeding technology and biotechnology. More specifically, using the quantitative trait locus (QTL) methods genes related to phytoaccumulation may be identified (Filatov et al. 2007) and subsequently genetic engineering may be used to enhance the accumulation behavior of B. juncea. Potential areas where B. juncea may be used as a tool of phytoaccumulation have been suggested in Map 3. This suggests that most of the arsenic contamination in Bangladeshi soil may be phytoaccumulated by B. juncea (Rai and BARI 11). The potential of use of B. juncea may be further increased by using some appropriate chelating agents (Schmoger et al. 2000). Moreover, prevalence of manmade causes to increase arsenic in soil and water should be reduced. For example, excessive use of groundwater for irrigation may be replaced by other forms of irrigation, especially deep tubewell irrigation, as studies suggest positive relation of presence of arsenic with depth level of tubewell (Hossain et al. 2005).
Association between Bone Morphogenetic Protein 2 Gene Polymorphisms and Skeletal Fluorosis of The Brick-tea Type Fluorosis in Tibetans and Kazakhs, China
Published in International Journal of Environmental Health Research, 2021
Qun Lou, Ning Guo, Wei Huang, Liaowei Wu, Mengyao Su, Yang Liu, Xiaona Liu, Bingyun Li, Yanmei Yang, Yanhui Gao
BMD in patients with chronic fluorosis shows various states from osteoporosis to osteoporosis, which can sometimes occur in the same patient. These phenomena are always caused by the varieties of bone mass and BMD. According to the previous studies, BMD or true bone density is established before birth and basically remains unchanged from birth to old age (Seeman 1997). It indicates that genetics play a dominated role of the BMD (Hegarty et al. 2018). Several quantitative trait locus studies have reported linkage of BMD in the general population to chromosomes 20p12, 14q22–23 and 6p11, i.e. (Karasik et al. 2002), where BMP2 is exactly located in 20p12 (Styrkarsdottir et al. 2003). In some cross-section studies have reported of BMP2 as a susceptibility gene for osteoporotic fractures and low BMD (McGuigan et al. 2007; Liao et al. 2019), but the inconsistent contribution for BMD in different populations also be found between SNPs in BMP2 (Choi et al. 2006; Ichikawa et al. 2006; Medici et al. 2006; Xiong et al. 2006). A study aimed at the U.S. Whites of European origin has reported that BMP2 rs235710 within the 5′-promoter region is highly suggestive of BMD and osteoporosis and rs1980499 link with ultradistal radius BMD (Xiong et al. 2006). Previous results had reported that BMP2 could promote the SF via activating wnt/β-catenin pathway (Chen and Yu 2014). In vitro studies had demonstrated that exposure of NaF could promote cells proliferation mediated with BMP2 (Huo et al. 2013; Wei et al. 2014). However, none of the studies have reported the association between BMP2 polymorphism and SF. Therefore, in this study, we focus on the association between BMP2 single nucleotide polymorphisms and SF. The study evaluated the effect of four selected SNPs of BMP2 on the risk of skeletal fluorosis. Because the distribution of Rs1980499 was deviation from HWE in Kazakhs controls, we only analyzed the other three SNPs in BMP2. The results have shown no association in all three SNPs with brick-tea-type SF in our subjects.