Genome-Wide Association Studies (GWAS) are widely used in the genetic dissection of complex traits. Most existing methods are based on single-marker association in genome-wide scans with population structure and polygenic background controls. To control the false positive rate, the Bonferroni correction for multiple tests is frequently adopted. This stringent correction results in the exclusion of important loci, especially for GWAS in crop genetics. To address this issue, multi-locus GWAS methodologies have been recommended, i.e., FASTmrEMMA, ISIS EM-BLASSO, mrMLM, FASTmrMLM, pLARmEB, pKWmEB and FarmCPU.
In this Research Topic, our purpose is to clarify some important issues in the application of multi-locus GWAS methods. We welcome the submission of Original Research Articles on the following subjects:
• First, we welcome manuscripts describing the advantages of new multi-locus GWAS methods over the widely-used single-locus GWAS methods in the genetic dissection of complex traits, metabolites and gene expression levels.
• Large experiment error in the field measurement of phenotypic values for complex traits in crop genetics results in relatively large P-values in GWAS, indicating the existence of small number of significantly associated SNPs. To solve this issue, a less stringent P-value critical value is often adopted, i.e., 0.001, 0.0001 and 1/m (m is the number of markers). Although lowering the stringency with which an association is made could identify more hits, confidence in these hits would significantly drop. In this Research Topic we welcome manuscripts focused on new technical clues and methods to obtain high power and low false positive rate in GWAS.
• Third, heritability missing in GWAS is a common phenomenon. Although a series of authors has explained the reasons why the heritability is missing, we feel that it is necessary to discuss how to further address this issue by means of multi-locus GWAS methods.
• Finally, we welcome authors to submit manuscripts that explain how to use these multi-locus GWAS methods and how to select them.
Genome-Wide Association Studies (GWAS) are widely used in the genetic dissection of complex traits. Most existing methods are based on single-marker association in genome-wide scans with population structure and polygenic background controls. To control the false positive rate, the Bonferroni correction for multiple tests is frequently adopted. This stringent correction results in the exclusion of important loci, especially for GWAS in crop genetics. To address this issue, multi-locus GWAS methodologies have been recommended, i.e., FASTmrEMMA, ISIS EM-BLASSO, mrMLM, FASTmrMLM, pLARmEB, pKWmEB and FarmCPU.
In this Research Topic, our purpose is to clarify some important issues in the application of multi-locus GWAS methods. We welcome the submission of Original Research Articles on the following subjects:
• First, we welcome manuscripts describing the advantages of new multi-locus GWAS methods over the widely-used single-locus GWAS methods in the genetic dissection of complex traits, metabolites and gene expression levels.
• Large experiment error in the field measurement of phenotypic values for complex traits in crop genetics results in relatively large P-values in GWAS, indicating the existence of small number of significantly associated SNPs. To solve this issue, a less stringent P-value critical value is often adopted, i.e., 0.001, 0.0001 and 1/m (m is the number of markers). Although lowering the stringency with which an association is made could identify more hits, confidence in these hits would significantly drop. In this Research Topic we welcome manuscripts focused on new technical clues and methods to obtain high power and low false positive rate in GWAS.
• Third, heritability missing in GWAS is a common phenomenon. Although a series of authors has explained the reasons why the heritability is missing, we feel that it is necessary to discuss how to further address this issue by means of multi-locus GWAS methods.
• Finally, we welcome authors to submit manuscripts that explain how to use these multi-locus GWAS methods and how to select them.