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45 results for “; genome-wide association mapping”
Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity
<p>A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French <em>Arabidopsis thaliana</em> population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.</p>
Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity
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Genome-wide association mapping to identify genetic loci for cold tolerance and cold recovery during germination in rice
<p>To investigate the genetic architecture underlying cold tolerance during germination in rice (<i>Oryza sativa</i>), we conducted a genome-wide association study (GWAS) using a novel diversity panel of 257 rice accessions from around the world and 5,185 SNP markers from a 7K SNP marker array. Genotyping was performed using a 7K Illumina iSelect custom-designed array by following the Infinium HD Array Ultra Protocol. The 7K array, called the C7AIR, was designed by Dr. Susan McCouch's Lab at Cornell University and consists of 7,098 SNPs (Morales et al. 2020, under review). After genotyping 257 rice accessions with the 7K array (C7AIR), poor-performing SNP markers (SNPs of call rate <90%; minor allele frequency <5%; or heterozygosity >20%) were removed from the dataset. For our study, a subset of 5,185 high-quality SNP markers obtained after filtering was used to perform the genome-wide association analysis. The dataset representing the genotype data of 5,185 SNP markers by 257 rice accessions is presented here.</p>
Genome-wide association mapping to identify genetic loci for cold tolerance and cold recovery during germination in rice
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Supplemental material for: Genome-wide association study and fine-mapping using imputed sequences to prioritize candidate genes for 30 complex traits in 50,309 Holstein bulls
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Data from: Genome-wide SNP identification and association mapping for seed mineral concentration in Mung bean (Vigna radiata L.)
<p><span><span>Mung bean (<i>Vigna radiata</i> L.) quality is dependent on seed chemical composition, which in turn determines the benefits of mung bean consumption for human health. While rich in a range of nutritional components, such as protein, macro- and micro- nutrients, carbohydrates and vitamins, mung bean remains less well studied than other legume crops. Mung bean genomics and genetic resources are relatively sparse. To further improve nutritional levels of mung bean grain requires genome-wide marker system tools. The objectives of this research were to develop these tools and conduct nutrient analysis in order to 1) identify single nucleotide polymorphisms (SNPs) using genotyping by sequencing (GBS) and to 2) perform genome-wide association studies (GWAS) for levels of calcium, iron, potassium, manganese, phosphorous, sulfur, and zinc in mung bean grain produced over two years of field experiment. A total of 112 GWAS models were explored using 6,486 high quality SNPs discovered in 92 cultivated mung bean accessions chosen from USDA core collection that represented 13 countries. The data obtained allowed for the identification of 43 associated SNPs and 20 main genomic regions that explained on average 22 % of the overall variation in seed macro- and micro- nutrients concentration on the basis of a multiple-year analysis. Most of the regions discovered in this study provide valuable candidate gene to use in future breeding of new varieties of mung bean with novel nutritional properties. Identification of the <a>underlying genes</a> will help to reveal the genetic control of mung bean seed nutritional property. Other SNPs identified in this study will serve as important resources to enable marker-assisted selection (MAS) in the species <i>V</i>. <i>radiata</i>, including wide and narrow crosses with / between cultivated and wild mung bean.</span></span></p>
Data from: Genome-wide association mapping of resistance to Septoria nodorum leaf blotch in a Nordic spring wheat collection
Parastagonospora nodorum is the causal agent of septoria nodorum blotch (SNB) in wheat. It is the most important leaf blotch pathogen in Norwegian spring wheat. Several quantitative trait loci (QTL) for SNB susceptibility have been identified. Some of these QTL are the result of underlying gene-for-gene interactions involving necrotrophic effectors (NEs) and corresponding sensitivity (Snn) genes. A collection of diverse spring wheat lines was evaluated for SNB resistance/susceptibility over seven growing seasons in the field. In addition, wheat seedlings were inoculated and infiltrated with culture filtrates (CFs) from four single spore isolates and infiltrated with semi-purified NEs (SnToxA, SnTox1 and SnTox3) under greenhouse conditions. In adult plants, the most stable SNB resistance QTL were located on 2B, 2D, 4A, 4B, 5A, 6B, 7A and 7B. The QTL on 2D was effective most years in the field. At the seedling stage, the most significant QTL after inoculation were located on 1A, 1B, 3A, 4B, 5B, 6B, 7A and 7B. The QTL on 3A and 6B were significant both after inoculation and CF infiltration, indicating the presence of novel NE-Snn interactions. The QTL on 4B and 7A were significant in both seedlings and adult plants. Correlations between SnToxA sensitivity and disease severity in the field were significant. To our knowledge, this is the first genome wide association mapping study (GWAS) to investigate SNB resistance at the adult plant stage under field conditions.
Data from: Genome-wide association mapping of phenotypic traits subject to a range of intensities of natural selection in Timema cristinae
The genetic architecture of adaptive traits can reflect the evolutionary history of populations and also shape divergence among populations. Despite this central role in evolution, relatively little is known regarding the genetic architecture of adaptive traits in nature, particularly for traits subject to known selection intensities. Here we quantitatively describe the genetic architecture of traits that are subject to known intensities of differential selection between host plant species in Timema cristinae stick insects. Specifically, we used phenotypic measurements of 10 traits and 211,004 single-nucleotide polymorphisms (SNPs) to conduct multilocus genome-wide association mapping. We identified a modest number of SNPs that were associated with traits and sometimes explained a large proportion of trait variation. These SNPs varied in their strength of association with traits, and both major and minor effect loci were discovered. However, we found no relationship between variation in levels of divergence among traits in nature and variation in parameters describing the genetic architecture of those same traits. Our results provide a first step toward identifying loci underlying adaptation in T. cristinae. Future studies will examine the genomic location, population differentiation, and response to selection of the trait-associated SNPs described here.
Genome-wide association mapping for component traits of drought and heat tolerance in wheat
<p>The study material in GWAS panel with 282 advanced breeding line of bread wheat genotypes from IARI stress breeding program was selected to map the genomic regions responsible for Drought and heat tolerance component traits.</p> <p>Phenotypic data:</p> <p>The GWAS panel was evaluated at multiple locations namely, IARI, New Delhi - DL (28.6550° N, 77.1888° E, MSL 228.61 m), ARI, Pune - PUNE (18.5204° N, 73.8567° E, MSL 560m), IIWBR, Karnal - IIWBR (29.6857° N, 76.9905°E, MSL 243m), IARI, Jharkhand - JR (24.1929° N, 85.3756° E, MSL 580m) and IARI RS, Indore - IND (22.7196° N, 75.8577° E, MSL 553 m) with augmented RCBD design. Three tratments viz, IR (Irrigated), RI (Restricted irrigated) and LS (Late sown) were imposed for control, drought and heat stress, respectively. Data was collected on traits like Days to heading (DH), Days to maturity (DM), Normalized Difference Vegetation Index (NDVI) at anthesis and grain filling stage, chlorophyll content (SPAD) of flag leaf at post anthesis stage, Plant height (PH), Canopy temperature (CT), Grain weight per spike (GWPS), Thousand Grain weight (TGW), Plot Yield (PLTY) and Biomass.</p> <p>Genotypic data:</p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 10546 high-quality SNPs was used in GWAS analysis.</p> <p>The detailed information of the methods and software used, data analysis and GWAS is provided at doi: 10.3389/fpls.2022.943033</p>
Data from: Genome-wide association mapping of date palm fruit traits
Date palms (Phoenix dactylifera) are an important fruit crop of arid regions of the Middle East and North Africa. Despite its importance, few genomic resources exist for date palms, hampering evolutionary genomic studies of this perennial crop species. Here we report an improved long-read genome assembly for P. dactylifera that is 772.3 Mb in length, with contig N50 of 897.2 Kb, and use this to perform GWAS mapping of the sex determining region and 21 fruit traits. We find a fruit color GWAS at the R2R3-MYB transcription factor (VIRESCENS) gene and identify functional alleles that include a retrotransposon insertion and start codon mutation. We also find a GWAS peak for sugar composition spanning deletion polymorphisms in multiple linked invertase genes. MYB transcription factors and invertase are implicated in fruit color and sugar composition in other crop species, demonstrating the importance of parallel evolution in the evolutionary diversification of domesticated species.
Data from: Genome-wide association mapping of resistance to Septoria nodorum leaf blotch in a Nordic spring wheat collection
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Data from: Genome-wide association mapping of date palm fruit traits
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Data from: Genome-wide association and regional heritability mapping to identify loci underlying variation in nematode resistance and body weight in Scottish Blackface lambs
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Data from: Use of a natural hybrid zone for genome-wide association mapping of craniofacial traits in the house mouse
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Data from: Genome-wide association mapping of phenotypic traits subject to a range of intensities of natural selection in Timema cristinae
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Data from: Genome-wide SNP identification and association mapping for seed mineral concentration in Mung bean (Vigna radiata L.)
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Genome-wide association mapping for component traits of drought and heat tolerance in wheat
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Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations
<p>Data set linked to the paper, "Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations". Pre-print of the paper is here: <a href="https://doi.org/10.1101/2020.06.16.146803">https://doi.org/10.1101/2020.06.16.146803</a>.</p> <p> </p> <p>cross_cancer_sum_stats.txt.gz contains summary genome-wide association statistics for susceptibility to single cancers (breast (BR), prostate (PR), ovarian (OV), endometrial (EN), estrogen receptor (ER)-positive breast (POS), ER-negative breast (NEG), and high-grade serous ovarian (HGS) cancers) and from the cross-cancer meta-analysis (main [main] and subtype-focused [sub]). EA in the header refers to the effect allele, OA is the other allele, EAF is the effect allele frequency in the largest of the single cancer data sets (BR), IMPR2 is the imputation quality in the largest of the single cancer data sets (BR), SE is the standard error, PVAL is the P-value, RE2Cs1 is the RE2C statistic mean effect part, RE2Cs2 is the RE2C statistic heterogeneity part, RE2Cp* is the RE2C* P-value. More on RE2Cp* can be found here: <a href="http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite">http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite</a> and in <a href="https://academic.oup.com/bioinformatics/article/33/14/i379/3953957">https://academic.oup.com/bioinformatics/article/33/14/i379/3953957</a> SNP names in cross_cancer_sum_stats.txt.gz include the chromosome and build 37 position.</p> <p> </p> <p>main_tetrachoric_corr_matrix.txt and subtype_tetrachoric_corr_matrix.txt provide the tetrachoric correlation matrices used in the main and subtype-focused meta-analyses. These were also used to specify the cryptic.cor argument of the exh.abf function of MetABF. More on MetABF can be found here: <a href="https://github.com/trochet/metabf">https://github.com/trochet/metabf</a> and in <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202">https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202</a></p> <p> </p> <p>prior_sigmas_for_metabf.txt contains the values used to specify the prior.sigma argument of the exh.abf function in MetABF.</p> <p> </p> <p>The breast cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29059683/"><strong>PMID 29059683</strong></a> and can be downloaded from <a href="http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/">http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/</a> (this link also includes acknowledgements). The prostate cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29892016/"><strong>PMID 29892016</strong></a> and can be downloaded from: <a href="http://practical.icr.ac.uk/blog/?page_id=8164">http://practical.icr.ac.uk/blog/?page_id=8164</a> (this link also includes acknowledgements). The ovarian cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/28346442/"><strong>PMID 28346442</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST004415">https://www.ebi.ac.uk/gwas/studies/GCST004415</a>. The endometrial cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/30093612/"><strong>PMID 30093612</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST006464">https://www.ebi.ac.uk/gwas/studies/GCST006464</a>. These links point to the same data that form the basis of the cross_cancer_sum_stats.txt.gz file.</p> <p> </p> <p><strong>The sample size and precision of the data presented should preclude identification of any individual study participant. However, in downloading these data, you undertake not to attempt to identify individual study participant and not to re-post these data to a third-party website. Please cite the PMIDs highlighted above along with the appropriate acknowledements if you use the cross_cancer_sum_stats.txt.gz file.</strong></p> <p> </p> <p>If you have any questions about this repository, please email Siddhartha Kar at siddhartha dot kar at bristol dot ac dot uk</p>
Data from: Genome-wide association mapping of quantitative traits in a breeding population of sugarcane
Background: Molecular markers associated with relevant agronomic traits could significantly reduce the time and cost involved in developing new sugarcane varieties. Previous sugarcane genome-wide association analyses (GWAS) have found few molecular markers associated with relevant traits at plant-cane stage. The aim of this study was to establish an appropriate GWAS to find molecular markers associated with yield related traits consistent across harvesting seasons in a breeding population. Sugarcane clones were genotyped with DArT (Diversity Array Technology) and TRAP (Target Region Amplified Polymorphism) markers, and evaluated for cane yield (CY) and sugar content (SC) at two locations during three successive crop cycles. GWAS mapping was applied within a novel mixed-model framework accounting for population structure with Principal Component Analysis scores as random component. Results: A total of 43 markers significantly associated with CY in plant-cane, 42 in first ratoon, and 41 in second ratoon were detected. Out of these markers, 20 were associated with CY in 2 years. Additionally, 38 significant associations for SC were detected in plant-cane, 34 in first ratoon, and 47 in second ratoon. For SC, one marker-trait association was found significant for the 3 years of the study, while twelve markers presented association for 2 years. In the multi-QTL model several markers with large allelic substitution effect were found. Sequences of four DArT markers showed high similitude and e-value with coding sequences of Sorghum bicolor, confirming the high gene microlinearity between sorghum and sugarcane. Conclusions: In contrast with other sugarcane GWAS studies reported earlier, the novel methodology to analyze multi-QTLs through successive crop cycles used in the present study allowed us to find several markers associated with relevant traits. Combining existing phenotypic trial data and genotypic DArT and TRAP marker characterizations within a GWAS approach including population structure as random covariates may prove to be highly successful. Moreover, sequences of DArT marker associated with the traits of interest were aligned in chromosomal regions where sorghum QTLs has previously been reported. This approach could be a valuable tool to assist the improvement of sugarcane and better supply sugarcane demand that has been projected for the upcoming decades.
Data from: Linkage disequilibrium clustering-based approach for association mapping with tightly linked genome-wide data
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Annotated Behaviour and Observability Dataset (ABODe)
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