Genome-wide association studies of grain quality traits in maize.
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ABSTRACT:
SUBMITTER: Zheng Y
PROVIDER: S-EPMC8105333 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The main goal of this study was to investigate the genetic basis of yield and grain quality traits in winter wheat genotypes using association mapping approach, and identify linked molecular markers for marker assisted selection. A total of 120 elite facultative/winter wheat genotypes were evaluated for yield, quality and other agronomic traits under rain-fed and irrigated conditions for two years (2011-2012) at the Tel Hadya station of ICARDA, Syria. The same genotypes were genotyped using 3,051 Diversity Array Technologies (DArT) markers, of which 1,586 were of known chromosome positions. The grain yield performance of the genotypes was highly significant both in rain-fed and irrigated sites. Average yield of the genotypes ranged from 2295 to 4038 kg/ha and 4268 to 7102 kg/ha under rain-fed and irrigated conditions, respectively. Protein content and alveograph strength (W) ranged from 13.6-16.1% and 217.6-375 Jx10-4, respectively. DArT markers wPt731910 (3B), wPt4680 (4A), wPt3509 (5A), wPt8183 (6B), and wPt0298 (2D) were significantly associated with yield under rain-fed conditions. Under irrigated condition, tPt4125 on chromosome 2B was significantly associated with yield explaining about 13% of the variation. Markers wPt2607 and wPt1482 on 5B were highly associated with protein content and alveograph strength explaining 16 and 14% of the variations, respectively. The elite genotypes have been distributed to many countries using ICARDA's International system for potential direct release and/or use as parents after local adaptation trials by the NARSs of respective countries. The QTLs identified in this study are recommended to be used for marker assisted selection after through validation using bi-parental populations.
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Project description:BackgroundRoot system architecture (RSA), which is determined by the crown root angle (CRA), crown root diameter (CRD), and crown root number (CRN), is an important factor affecting the ability of plants to obtain nutrients and water from the soil. However, the genetic mechanisms regulating crown root traits in the field remain unclear.MethodsIn this study, the CRA, CRD, and CRN of 316 diverse maize inbred lines were analysed in three field trials. Substantial phenotypic variations were observed for the three crown root traits in all environments. A genome-wide association study was conducted using two single-locus methods (GLM and MLM) and three multi-locus methods (FarmCPU, FASTmrMLM, and FASTmrEMMA) with 140,421 SNP.ResultsA total of 38 QTL including 126 SNPs were detected for CRA, CRD, and CRN. Additionally, 113 candidate genes within 50 kb of the significant SNPs were identified. Combining the gene annotation information and the expression profiles, 3 genes including GRMZM2G141205 (IAA), GRMZM2G138511 (HSP) and GRMZM2G175910 (cytokinin-O-glucosyltransferase) were selected as potentially candidate genes related to crown root development. Moreover, GRMZM2G141205, encoding an AUX/IAA transcriptional regulator, was resequenced in all tested lines. Five variants were identified as significantly associated with CRN in different environments. Four haplotypes were detected based on these significant variants, and Hap1 has more CRN.ConclusionsThese findings may be useful for clarifying the genetic basis of maize root system architecture. Furthermore, the identified candidate genes and variants may be relevant for breeding new maize varieties with root traits suitable for diverse environmental conditions.
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Project description:This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts and directions are provided to facilitate others in this process.
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Project description:Microarray-based markers such as Diversity Arrays Technology (DArT) have become the genetic markers of choice for construction of high-density maps, quantitative trait loci (QTL) mapping and genetic diversity analysis based on their efficiency and low cost. More recently, the DArT technology was further developed in combination with high-throughput next-generation sequencing (NGS) technologies to generate the DArTseq platform representing a new sequencing tool of complexity-reduced representations. In this study, we used DArTseq markers to investigate genetic diversity and genome-wide association studies (GWAS) of grain quality traits in rice (Oryza sativa L.). The study was performed using 59 rice genotypes with 525 SNPs derived from DArTseq platform. Population structure analysis revealed only two distinct genetic clusters where genotypes were grouped based on environmental adaptation and pedigree information. Analysis of molecular variance indicated a low degree of differentiation among populations suggesting the need for broadening the genetic base of the current germplasm collection. GWAS revealed 22 significant associations between DArTseq-derived SNP markers and rice grain quality traits in the test genotypes. In general, 2 of the 22 significant associations were in chromosomal regions where the QTLs associated with the given traits had previously been reported, the other 20 significant SNP marker loci were indicative of the likelihood discovery of novel alleles associated with rice grain quality traits. DArTseq-derived SNP markers that include SNP12_100006178, SNP13_3052560 and SNP14_3057360 individually co-localised with two functional gene groups that were associated with QTLs for grain width and grain length to width ratio on chromosome 3, indicating trait dependency or pleiotropic-effect loci. This study demonstrated that DArTseq markers were useful genomic resources for genome-wide association studies of rice grain quality traits to accelerate varietal development and release.
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Project description:Genome-wide association studies of discrete traits generally use simple methods of analysis based on chi(2) tests for contingency tables or logistic regression, at least for an initial scan of the entire genome. Nevertheless, more power might be obtained by using various methods that analyze multiple markers in combination. Methods based on sliding windows, wavelets, Bayesian shrinkage, or penalized likelihood methods, among others, were explored by various participants of Genetic Analysis Workshop 16 Group 1 to combine information across multiple markers within a region, while others used Bayesian variable selection methods for genome-wide multivariate analyses of all markers simultaneously. Imputation can be used to fill in missing markers on individual subjects within a study or in a meta-analysis of studies using different panels. Although multiple imputation theoretically should give more robust tests of association, one participant contribution found little difference between results of single and multiple imputation. Careful control of population stratification is essential, and two contributions found that previously reported associations with two genes disappeared after more precise control. Other issues considered by this group included subgroup analysis, gene-gene interactions, and the use of biomarkers.
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Project description:The availability of affordable high throughput technology for parallel genotyping has opened the field of genetics to genome-wide association studies (GWAS), and in the last few years hundreds of articles reporting results of GWAS for a variety of heritable traits have been published. What do these results tell us? Although GWAS have discovered a few hundred reproducible associations, this number is underwhelming in relation to the huge amount of data produced, and challenges the conjecture that common variants may be the genetic causes of common diseases. We argue that the massive amount of genetic data that result from these studies remains largely unexplored and unexploited because of the challenge of mining and modeling enormous data sets, the difficulty of using nontraditional computational techniques and the focus of accepted statistical analyses on controlling the false positive rate rather than limiting the false negative rate. In this article, we will review the common approach to analysis of GWAS data and then discuss options to learn more from these data. We will use examples from our ongoing studies of sickle cell anemia and also GWAS in multigenic traits.
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Project description:The diversity of metabolites found in plants is by far greater than in most other organisms. Metabolic profiling techniques, which measure many of these compounds simultaneously, enabled investigating the regulation of metabolic networks and proved to be useful for predicting important agronomic traits. However, little is known about the genetic basis of metabolites in crops such as maize. Here, a set of 289 diverse maize inbred lines was genotyped with 56,110 SNPs and assayed for 118 biochemical compounds in the leaves of young plants, as well as for agronomic traits of mature plants in field trials. Metabolite concentrations had on average a repeatability of 0.73 and showed a correlation pattern that largely reflected their functional grouping. Genome-wide association mapping with correction for population structure and cryptic relatedness identified for 26 distinct metabolites strong associations with SNPs, explaining up to 32.0% of the observed genetic variance. On nine chromosomes, we detected 15 distinct SNP-metabolite associations, each of which explained more then 15% of the genetic variance. For lignin precursors, including p-coumaric acid and caffeic acid, we found strong associations (P values to ) with a region on chromosome 9 harboring cinnamoyl-CoA reductase, a key enzyme in monolignol synthesis and a target for improving the quality of lignocellulosic biomass by genetic engineering approaches. Moreover, lignin precursors correlated significantly with lignin content, plant height, and dry matter yield, suggesting that metabolites represent promising connecting links for narrowing the genotype-phenotype gap of complex agronomic traits.
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Project description:Using genome-wide association mapping, 47 SNPs within 27 significant loci were identified for four grain shape traits, and 424 candidate genes were predicted from public database. Grain shape is a key determinant of grain yield and quality in rice (Oryza sativa L.). However, our knowledge of genes controlling rice grain shape remains limited. Genome-wide association mapping based on linkage disequilibrium (LD) has recently emerged as an effective approach for identifying genes or quantitative trait loci (QTL) underlying complex traits in plants. In this study, association mapping based on 5291 single nucleotide polymorphisms (SNPs) was conducted to identify significant loci associated with grain shape traits in a global collection of 469 diverse rice accessions. A total of 47 SNPs were located in 27 significant loci for four grain traits, and explained ~44.93-65.90 % of the phenotypic variation for each trait. In total, 424 candidate genes within a 200 kb extension region (±100 kb of each locus) of these loci were predicted. Of them, the cloned genes GS3 and qSW5 showed very strong effects on grain length and grain width in our study. Comparing with previously reported QTLs for grain shape traits, we found 11 novel loci, including 3, 3, 2 and 3 loci for grain length, grain width, grain length-width ratio and thousand grain weight, respectively. Validation of these new loci would be performed in the future studies. These results revealed that besides GS3 and qSW5, multiple novel loci and mechanisms were involved in determining rice grain shape. These findings provided valuable information for understanding of the genetic control of grain shape and molecular marker assistant selection (MAS) breeding in rice.
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