Dataset Information

Functional models in genome-wide selection.

ABSTRACT: The development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, three scenarios were analyzed: F2 population with 300 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 12,150 SNP markers that were distributed through ten linkage groups; F? populations with 320 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 10,020 SNP markers that were distributed through ten linkage groups; and data related to Eucalyptus spp. to measure the model performance in a real LD setting, with 611 individuals whose phenotypes were simulated from QTLs distributed through a panel of 36,812 SNPs with known positions. The performance of the proposed method was compared with those of other genome selection models, namely, RR-BLUP, Bayes B and Bayesian Lasso. The Bayesian functional model presented higher or similar predictive ability when compared with those classical regressions methods in simulated and real scenarios on different LD structures. In general, the Bayesian functional model also achieved higher computational efficiency, using 12 SNPs per MCMC round. The model was efficient in the identification of causal regions and showed high flexibility of analysis, as it is easily adaptable to any genomic selection model.

SUBMITTER: Moura EG

PROVIDER: S-EPMC6808424 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Functional models in genome-wide selection.

Moura Ernandes Guedes EG Pamplona Andrezza Kellen Alves AKA Balestre Marcio M

PloS one 20191023 10

The development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, t ...[more]

PMID: 31644532

Similar Datasets

Project description:Motivation:Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci is associated with multiple traits-a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide single nucleic polymorphisms (SNPs) together. Results:We develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially non-informative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP in terms of both high association mapping power and accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. Availability and implementation:iMAP is freely available at http://www.xzlab.org/software.html. Supplementary information:Supplementary data are available at Bioinformatics online.

Project description:"Background: The ascomycete Fusarium fujikuroi causes “bakanae” disease of rice due to its ability to produce gibberellins (GA), but is also known for producing a broad spectrum of other secondary metabolites, (SM) such as pigments and harmful mycotoxins. Biosynthesis of multiple SM is regulated by nitrogen availability. The global GATA transcription factors (TF) AreA and AreB were shown to play key roles in this nitrogen-dependent regulation. Recently, we have shown that AreA activates the transcription of AREB, but not vice versa. Both regulators interact in the nucleus, indicating a complex regulatory interplay between both regulators. Results: We performed transcriptome and proteome analyses of the wild type and ΔAREA and ΔAREB mutants under nitrogen limiting and sufficient conditions. Microarray analysis revealed that each transcription factor regulates a total set of about 4,200 genes under nitrogen limiting conditions. Gene enrichment analysis revealed that significantly up- and down-regulated genes belong to functional classes of primary/secondary metabolism, transporters, cell defense/virulence and transcription. Under nitrogen excess, AreA is not active as a transcriptional regulator, while AreB affects expression of about 4,400 genes. Both transcription factors regulate a common set of about 2,100 genes, often coordinately either as positive or as negative regulators. Our proteomics analysis identified 446 AreA-regulated and 386 AreB-regulated proteins under nitrogen limitation and 138 AreA-regulated and 447 AreB-regulated proteins under nitrogen sufficiency, respectively. Comparison of the proteome and transcriptome data indicates that both GATA-factors not only regulate expression of target genes but also affect protein abundance by post-transcriptional regulations, for example on enzymes involved in glutamate cycling, as well as histone acetylation at some of the target SM gene clusters. Conclusion: This study provides novel insights into the AreA- and AreB-mediated regulation network on a cellular scale. Microarray analysis clearly demonstrated that both GATA-factors are major regulators of alternate nitrogen assimilatory pathways and secondary metabolism. Moreover, proteome analysis of the AREA and AREB deletion mutants showed that both TF are global players interconnecting carbon and nitrogen regulation. They mainly act as mutualistic, but also antagonistic regulators and operate by transcriptional and post-transcriptional regulations and epigenetic modifications." (Abstract taken from manuscript)

Dataset Information

Functional models in genome-wide selection.

Publications

Functional models in genome-wide selection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets