Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction

Ye, Shaopan; Li, Jiaqi; Zhang, Zhe

doi:10.1186/s40104-020-00515-5

Cited by 26 publications

(24 citation statements)

References 44 publications

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“…In species where reference genome or high-quality genome assembly is available, the incorporation of SNPs with biological functions ( i.e. , multi-omics) improved the prediction accuracy, for instance, about 5–19% for fertility traits in dairy cattle ( Abdollahi-Arpanahi et al 2017 ; Nani et al 2019 ), or 27.4–60.7% for traits in inbred lines of Drosophila ( Ye et al 2020 ). Unfortunately, a good genome assembly is currently not available for striped catfish ( Kim et al 2018 ), this area thus deserves future studies to enable the efficient utilization of genomic information in the genetic selection program for this economically important species in the aquaculture sector.…”

Section: Discussionmentioning

confidence: 99%

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Phuc

Oanh

et al. 2021

G3 Genes|Genomes|Genetics

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Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e.,, machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e.,, pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e.,, ML-KAML) and deep learning (i.e.,, DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e.,, survival status coded as 0 and 1 and survival time, i.e.,, days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318 - 400 SNPs for survival status and 1,362–1,589 SNPs for survival time) were somewhat lower (0.3 to 15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease resistant traits studied in this population of striped catfish P. hypophthalmus.

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Section: Discussionmentioning

confidence: 99%

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Phuc

Oanh

et al. 2021

G3 Genes|Genomes|Genetics

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“…Such behavior of eQTLs in this study was observed only for a single phenotype with low resolution genotyping data. Also, Ye et al (2020) were successful in improving the performance of phenotype prediction in Drosophila using genotypes of eQTLs regulating genes that are important for the phenotype. They proceeded with successive selection steps involving a transcriptome wide association study (TWAS) with an eQTLs analysis for the TWAS significant genes, while optimizing the detection thresholds of these two analyses.…”

Section: Integration Success Is Driven By the Loss In Importance Of Covariation Sources Between Genomics And Transcriptomicsmentioning

confidence: 99%

“…Such integration can happen for a given omic type over different datasets or populations, each one summarized by its own model, with a final global model feeding on the top features contributed by each of the initial models. Another variant of the same model-based integration proceeds through a multistage approach, combining sequentially different omics for a given population (Ye et al, 2020). One of the simplest integration approaches, however, remains data concatenation (Azodi et al, 2020), by which multiple omics are placed side by side into a single large input matrix.…”

Section: Introductionmentioning

confidence: 99%

“…When the integration included a gene ontology (GO) category as an additional layer of information, accuracies were surprisingly improved (Morgante et al, 2020). Using the same Drosophila panel, however, Ye et al (2020) found some benefits by following a model-based integration approach, with a first modeling stage aiming at detecting SNP associated with transcripts (eQTLs), and a subsequent prediction model focused on resulting eQTLs.…”

Section: Introductionmentioning

confidence: 99%

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eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction

Wade

Duruflé

Sanchez

et al. 2021

Preprint

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Multi-omics represent a promising link between phenotypes and genome variation. Few studies yet address their integration to understand genetic architecture and improve predictability. Our study used 241 poplar genotypes, phenotyped in two common gardens, with their xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic and transcriptomic datasets. For each trait, prediction models were built with genotypic or transcriptomic data and compared to concatenation integrating both omics. The advantage of integration varied across traits and, to understand such differences, we made an eQTL analysis to characterize the interplay between the genome and the transcriptome and classify the predicting features into CIS or TRANS relationships. A strong and significant negative correlation was found between the change in predictability and the change in predictor importance for eQTLs (both TRANS and CIS effects) and CIS regulated transcripts, and mostly for traits showing beneficial integration and evaluated in the site of transcriptomic sampling. Consequently, beneficial integration happens when redundancy of predictors is decreased, leaving the stage to other less prominent but complementary predictors. An additional GO enrichment analysis appeared to corroborate such statistical output. To our knowledge, this is a novel finding delineating a promising way to explore data integration.

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“…Several multi-omics prediction studies have been reported in cereal and animal species (Guo et al 2016 ; Riedelsheimer et al 2012 ; Schrag et al 2018 ; Wang et al 2019 ; Westhues et al 2017 ; Xu et al 2017 ; Xu et al 2021 ; Ye et al 2020 ; Zhao et al 2015 ). These studies have shed light on the merits of multi-omics prediction over traditional genomic prediction and discussed useful statistical methods for integrating omics data.…”

Section: Introductionmentioning

confidence: 99%

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Campbell

Yeats

et al. 2021

Theor Appl Genet

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Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Abstract Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

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Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction

Cited by 26 publications

References 44 publications

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

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