SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

Pérez‐Enciso, Miguel; Ramírez-Ayala, Lino C.; Zingaretti, Laura M.

doi:10.1186/s12711-020-0530-2

Cited by 13 publications

(6 citation statements)

References 27 publications

(33 reference statements)

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“…There is ample literature and software available on the simulation of ‘standard’ complex phenotypes, e.g., [48–51]. These algorithms, however, are not suited for some of the scenarios posed in Fig 1.…”

Section: Methodsmentioning

confidence: 99%

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Pérez‐Enciso

Zingaretti

Ramayo-Caldas

et al. 2020

Preprint

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The analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

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Section: Methodsmentioning

confidence: 99%

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Pérez‐Enciso

Zingaretti

Ramayo-Caldas

et al. 2020

Preprint

Self Cite

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“…PFNs have been fit using priors defined by Bayesian neural networks, Gaussian processes, and structural-causal models (Müller et al, 2021; Hollmann et al, 2022). In order to create priors which model plant and animal populations, we use genetic simulations based on SeqBreed (Pérez-Enciso et al, 2020). In the context of GPFNs, a prior is defined by a simulator which simulates a particular type of population (wild, mapping, breeding, etc.)…”

Section: Approachmentioning

confidence: 99%

“…For the trait of interest, we simulate both additive and dominance effects (with the proportion of dominant alleles randomly sampled). Marker ef-fects are sampled from a gamma distribution (Pérez-Enciso et al, 2020), with the α and β parameters randomly sampled. The trait heritability ( h 2 ), the number of QTN, the number of SNPs, the recombination rate, and other factors are also randomly sampled.…”

Section: Approachmentioning

confidence: 99%

GPFN: Prior-Data Fitted Networks for Genomic Prediction

Ubbens,

Stavness,

Sharpe

2023

Preprint

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Genomic Prediction (GP) methods predict the breeding value of unphenotyped individuals in order to select parental candidates in breeding populations. Among models for GP, classical linear models have remained consistently popular, while more complex nonlinear methods such as deep neural networks have shown comparable accuracy at best. In this work we propose the Genomic Prior-Data Fitted Network (GPFN), a new paradigm for GP. GPFNs perform amortized Bayesian inference by drawing hundreds of thousands or millions of synthetic breeding populations during the prior fitting phase. This allows GPFNs to be deployed without requiring any training or tuning, providing predictions in a single inference pass. On three populations of crop plants across two different crop species, GPFNs perform significantly better than the linear baseline on 13 out of 16 traits. On a challenging between-families NAM prediction task, the GPFN performs significantly better in 3 locations while only falling behind in one. GPFNs represent a completely new direction for the field of genomic prediction, and have the potential to unlock levels of selection accuracy not possible with existing methods.

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“…In both simulations, we used as a founder population a subset of 400 SNP and 150 individuals from tetraploid potato data described by [Enciso-Rodriguez et al, 2018]. To create both simulations of phenotypes, we sampled either additive or dominant effects from a gamma distribution Γ (shape = 0.2 and scale = 5) and specified ten SNPs as causal SNPs along with their effects under the Phyton3 SeqBreed software [Pérez-Enciso et al, 2020], inspired in the pSBVB software created to generate polyploid data [Zingaretti et al, 2019].…”

Section: Testing Multigwasmentioning

confidence: 99%

MultiGWAS: An integrative tool for Genome Wide Association Studies (GWAS) in tetraploid organisms

Garreta

Cerón‐Souza

Palacio

et al. 2020

Preprint

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The Genome-Wide Association Studies (GWAS) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of model and non-model organisms. For this research question, current practice is the replication of the GWAS testing different parameters and models to validate the reproducibility of results. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software, two for polyploid data (GWASpoly and SHEsis) and two for diploids data (PLINK and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in an interface. It manages different genotype formats, including VCF. It executes both the full and naive models using several quality filters. Besides, it calculates a score to choose the best gene action model across GWASPoly and TASSEL. Finally, it generates several reports that facilitate the identification of false associations from both the significant and the best-ranked association SNP among the four software. We tested MultiGWAS with tetraploid potato data. The execution demonstrated that the Venn diagram and the other companion reports (i.e., Manhattan and QQ plots, heatmaps for associated SNP profiles, and chord diagrams to trace associated SNP by chromosomes) were useful to identify associated SNP shared among different models and parameters. Therefore, we confirmed that MultiGWAS is a suitable wrapping tool that successfully handles GWAS replication in both diploid and tetraploid organisms.

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SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

Cited by 13 publications

References 27 publications

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Opportunities and limits of combining microbiome and genome data for complex trait prediction

GPFN: Prior-Data Fitted Networks for Genomic Prediction

MultiGWAS: An integrative tool for Genome Wide Association Studies (GWAS) in tetraploid organisms

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