scholarly journals Unravelling consensus genomic regions associated with quality traits in wheat (Triticum aestivum L.) using meta-analysis of quantitative trait loci

2021 ◽  
Author(s):  
Santosh Gudi ◽  
Dinesh Kumar Saini ◽  
Gurjeet Singh ◽  
Priyanka Halladakeri ◽  
Mohammad Shamshad ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Meta Analysis ◽  
Starch Synthase ◽  
Dough Rheology ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Trait Loci ◽  
Gene Models

A meta-analysis of quantitative trait loci (QTLs) associated with following six major quality traits (i) arabinoxylan, (ii) dough rheology properties, (iii) nutritional traits, (iv) polyphenol content, (v) processing quality traits, and (vi) sedimentation volume was conducted in wheat. For this purpose, as many as 2458 QTLs were collected from the 50 mapping studies published during 2013-20. Of the total QTLs, 1126 QTLs were projected on to the consensus map saturated with 2,50,077 markers resulting into the identification of 110 meta-QTLs (MQTLs) with average confidence interval (CI) of 5.6 cM. These MQTLs had 18.84 times reduced CI compared to CI of initial QTLs. Fifty-one (51) MQTLs were also verified with the marker-trait associations (MTAs) detected in earlier genome-wide association studies (GWAS). Physical region occupied by a single MQTL ranged from 0.12 to 749.71 Mb with an average of 130.25 Mb. Candidate gene mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with >2 transcripts per million (TPM) expression in grains and related tissues which also included 44 high-confidence candidate genes known to be involved in the various cellular and biochemical processes related to quality traits. Further, nine functionally characterized wheat genes associated with grain protein content, high molecular weight glutenin and starch synthase enzymes were also found to be co-localized with some of the MQTLs. In addition, synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTLs syntenic to 16 rice MQTLs. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. These genes encoded proteins mainly belonging to the following families: starch synthase, glycosyl transferase, aldehyde dehydrogenase, SWEET sugar transporter, alpha amylase, glycoside hydrolase, glycogen debranching enzyme, protein kinase, peptidase, legumain and seed storage protein enzyme.

scholarly journals Identification, deployment, and transferability of quantitative trait loci from genome-wide association studies in plants

2020 ◽  
Vol 24 ◽  
pp. 100145 ◽  
Author(s):  
Mohsen Mohammadi ◽  
Alencar Xavier ◽  
Travis Beckett ◽  
Savannah Beyer ◽  
Liyang Chen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci

2019 ◽  
Vol 36 (5) ◽  
pp. 1517-1521
Author(s):  
Leilei Cui ◽  
Bin Yang ◽  
Nikolas Pontikos ◽  
Richard Mott ◽  
Lusheng Huang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genetic Effects ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Additive Effects ◽  
A Genome ◽  
Trait Loci ◽  
Additive Genetic Effects

Abstract Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

2017 ◽  
Author(s):  
Fanny Bonnafous ◽  
Ghislain Fievet ◽  
Nicolas Blanchet ◽  
Marie-Claude Boniface ◽  
Sébastien Carrère ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Flowering Time ◽  
Genetic Control ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Trait Loci ◽  
Genomic Regions

AbstractGenome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

scholarly journals Identification of putative effector genes across the GWAS Catalog using molecular quantitative trait loci from 68 tissues and cell types

2019 ◽  
Author(s):  
Cong Guo ◽  
Karsten B. Sieber ◽  
Jorge Esparza-Gordillo ◽  
Mark R. Hurle ◽  
Kijoung Song ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Effector Genes ◽  
Genome Wide ◽  
Trait Loci ◽  
Gwas Catalog

AbstractIdentifying the effector genes from genome-wide association studies (GWAS) is a crucial step towards understanding the biological mechanisms underlying complex traits and diseases. Colocalization of expression and protein quantitative trait loci (eQTL and pQTL, hereafter collectively called “xQTL”) can be effective for mapping associations to genes in many loci. However, existing colocalization methods require full single-variant summary statistics which are often not readily available for many published GWAS or xQTL studies. Here, we present PICCOLO, a method that uses minimum SNP p-values within a locus to determine if pairs of genetic associations are colocalized. This method greatly expands the number of GWAS and xQTL datasets that can be tested for colocalization. We applied PICCOLO to 10,759 genome-wide significant associations across the NHGRI-EBI GWAS Catalog with xQTLs from 28 studies. We identified at least one colocalized gene-xQTL in at least one tissue for 30% of associations, and we pursued multiple lines of evidence to demonstrate that these mappings are biologically meaningful. PICCOLO genes are significantly enriched for biologically relevant tissues, and 4.3-fold enriched for targets of approved drugs.

scholarly journals Modeling Allelic Diversity of Multi-parent Mapping Populations Affects Detection of Quantitative Trait Loci

2021 ◽  
Author(s):  
Sarah Odell ◽  
Asher I Hudson ◽  
Sébastien Praud ◽  
Pierre Dubreuil ◽  
Marie-Helene Tixier ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Flowering Time ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Allelic Diversity ◽  
Genome Wide Association Studies ◽  
Trait Loci ◽  
Mapping Populations

The search for quantitative trait loci (QTL) that explain complex traits such as yield and flowering time has been ongoing in all crops. Methods such as bi-parental QTL mapping and genome-wide association studies (GWAS) each have their own advantages and limitations. Multi-parent advanced generation intercross (MAGIC) populations contain more recombination events and genetic diversity than bi-parental mapping populations and reduce the confounding effect of population structure that is an issue in association mapping populations. Here we discuss the results of using a MAGIC population of doubled haploid (DH) maize lines created from 16 diverse founders to perform QTL mapping. We compare three models that assume bi-allelic, founder, and ancestral haplotype allelic states for QTL. The three methods have different power to detect QTL for a variety of agronomic traits. Although the founder approach finds the most QTL, there are also QTL unique to each method, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time QTL, qDTA8, which contains vgt1, suggests a potential epistatic interaction and highlights the strengths and weaknesses of each method. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and show the limitations of binary SNP data for identifying multi-allelic QTL.

scholarly journals Expression quantitative trait loci (eQTLs) in microRNA genes are enriched for schizophrenia and bipolar disorder association signals

2015 ◽  
Vol 45 (12) ◽  
pp. 2557-2569 ◽  
Author(s):  
V. S. Williamson ◽  
M. Mamdani ◽  
G. O. McMichael ◽  
A. H. Kim ◽  
D. Lee ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Quantitative Trait Loci ◽  
Mirna Expression ◽  
Quantitative Trait ◽  
Negative Impact ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Disease Etiology ◽  
Trait Loci ◽  
Postmortem Brains

BackgroundSchizophrenia (SZ) and bipolar disorder (BD) have substantial negative impact on the quality of human life. Both, microRNA (miRNA) expression profiling in SZ and BD postmortem brains [and genome-wide association studies (GWAS)] have implicated miRNAs in disease etiology. Here, we aim to determine whether significant GWAS signals observed in the Psychiatric Genetic Consortium (PGC) are enriched for miRNAs.MethodA two-stage approach was used to determine whether association signals from PGC affect miRNAs: (i) statistical assessment of enrichment using a Simes test and sum of squares test (SST) and (ii) biological evidence that quantitative trait loci (eQTL) mapping to known miRNA genes affect their expression in an independent sample of 78 postmortem brains from the Stanley Medical Research Institute.ResultsA total of 2567 independent single nucleotide polymorphisms (SNPs) (R2 > 0.8) were mapped locally, within 1 Mb, to all known miRNAs (miRBase v. 21). We show robust enrichment for SZ- and BD-related SNPs with miRNAs using Simes (SZ: p ≤ 0.0023, BD: p ≤ 0.038), which remained significant after adjusting for background inflation in SZ (empirical p = 0.018) and approached significance in BD (empirical p = 0.07). At a false discovery rate of 10%, we identified a total of 32 eQTLs to influence miRNA expression; 11 of these overlapped with BD.ConclusionsOur approach of integrating PGC findings with eQTL results can be used to generate specific hypotheses regarding the role of miRNAs in SZ and BD.

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