Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Accessing the underlying genetics of complex traits, especially in small grain pulses is an important breeding objective for crop improvement. Genome-wide association studies (GWAS) analyze thousands of genetic variants across several genomes to identify links with specific traits. This approach has discovered many strong associations between genes and traits, and the number of associated variants is expected to continue to increase as GWAS sample sizes increase. GWAS has a range of applications like understanding the genetic architecture associated with phenotype, estimating genetic correlation and heritability, developing genetic maps based on novel identified quantitative trait loci (QTLs)/genes, and developing hypotheses related to specific traits in the next generation. So far, several causative alleles have been identified using GWAS which had not been previously detected using QTL mapping. GWAS has already been successfully applied in mung bean () to identify SNPs/alleles that are used in breeding programs for enhancing yield and improvement against biotic and abiotic factors. In this review, we summarize the recently used advanced genetic tools, the concept of GWAS and its improvement in combination with structural variants, the significance of combining high-throughput phenotyping and genome editing with GWAS, and also highlights the genetic discoveries made with GWAS. Overall, this review explains the significance of GWAS with other advanced tools in the future, concluding with an overview of the current and future applications of GWAS with some recommendations.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688477 | PMC |
http://dx.doi.org/10.3389/fpls.2024.1436532 | DOI Listing |
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