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 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

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 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: present and future

2013 ◽  
Vol 368 (1620) ◽  
pp. 20120362 ◽  
Author(s):  
Alexandra C. Nica ◽  
Emmanouil T. Dermitzakis
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Sequence Variation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Expression Quantitative Trait Loci ◽  
Genome Wide ◽  
Trait Loci ◽  
Gene Expression Levels

The last few years have seen the development of large efforts for the analysis of genome function, especially in the context of genome variation. One of the most prominent directions has been the extensive set of studies on expression quantitative trait loci (eQTLs), namely, the discovery of genetic variants that explain variation in gene expression levels. Such studies have offered promise not just for the characterization of functional sequence variation but also for the understanding of basic processes of gene regulation and interpretation of genome-wide association studies. In this review, we discuss some of the key directions of eQTL research and its implications.

scholarly journals Epigenomes of Human Hearts Reveal New Genetic Variants Relevant for Cardiac Disease and Phenotype

2020 ◽  
Vol 127 (6) ◽  
pp. 761-777 ◽  
Author(s):  
Wilson Lek Wen Tan ◽  
Chukwuemeka George Anene-Nzelu ◽  
Eleanor Wong ◽  
Chang Jie Mick Lee ◽  
Hui San Tan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

Rationale: Identifying genetic markers for heterogeneous complex diseases such as heart failure is challenging and requires prohibitively large cohort sizes in genome-wide association studies to meet the stringent threshold of genome-wide statistical significance. On the other hand, chromatin quantitative trait loci, elucidated by direct epigenetic profiling of specific human tissues, may contribute toward prioritizing subthreshold variants for disease association. Objective: Here, we captured noncoding genetic variants by performing epigenetic profiling for enhancer H3K27ac chromatin immunoprecipitation followed by sequencing in 70 human control and end-stage failing hearts. Methods and Results: We have mapped a comprehensive catalog of 47 321 putative human heart enhancers and promoters. Three thousand eight hundred ninety-seven differential acetylation peaks (FDR [false discovery rate], 5%) pointed to pathways altered in heart failure. To identify cardiac histone acetylation quantitative trait loci (haQTLs), we regressed out confounding factors including heart failure disease status and used the G-SCI (Genotype-independent Signal Correlation and Imbalance) test 1 to call out 1680 haQTLs (FDR, 10%). RNA sequencing performed on the same heart samples proved a subset of haQTLs to have significant association also to gene expression (expression quantitative trait loci), either in cis (180) or through long-range interactions (81), identified by Hi-C (high-throughput chromatin conformation assay) and HiChIP (high-throughput protein centric chromatin) performed on a subset of hearts. Furthermore, a concordant relationship between the gain or disruption of TF (transcription factor)-binding motifs, inferred from alternative alleles at the haQTLs, implied a surprising direct association between these specific TF and local histone acetylation in human hearts. Finally, 62 unique loci were identified by colocalization of haQTLs with the subthreshold loci of heart-related genome-wide association studies datasets. Conclusions: Disease and phenotype association for 62 unique loci are now implicated. These loci may indeed mediate their effect through modification of enhancer H3K27 acetylation enrichment and their corresponding gene expression differences (bioRxiv: https://doi.org/10.1101/536763 ). Graphical Abstract: A graphical abstract is available for this article.

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