Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection

Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

doi:10.1186/s12711-017-0319-0

Cited by 63 publications

(51 citation statements)

References 62 publications

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“…features. If few (or even no) causal variants are contained in the genomic feature, the accuracy of GFBLUP will decrease because of too much weight being given to spurious genomic features (Fang et al, 2017). Similar results were also found in this study (Table 2).…”

Section: Genomic Feature Blup Model For Genomic Predictionsupporting

confidence: 88%

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

Zhang

2020

Preprint

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Background: Presently, multi-omics data (e.g., genomics, transcriptomics, proteomics, and metabolomics) are available for genomic prediction. Omics data not only offer new data layers for genomic prediction but also provide a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level. Therefore, using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction. In this study, simultaneously using whole-genome sequencing (WGS) and gene expression level data, four strategies for single-nucleotide polymorphism (SNP) preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel.Results: Using genomic best linear unbiased prediction (GBLUP) with complete WGS data, the prediction accuracy values were 0.208±0.020 (0.181±0.022) for the startle response and 0.272±0.017 (0.307±0.015) for starvation resistance in the female (male) lines. Compared with GBLUP using complete WGS data, both GBLUP and the genomic feature BLUP (GFBLUP) did not improve the prediction accuracy using SNPs preselected from the complete WGS data based on the results of genome-wide association studies (GWASs) or transcriptome-wide association studies (TWASs). Furthermore, by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus (eQTL) mapping of all genes, only the startle response had greater accuracy than GBLUP with the complete WGS data. The best accuracy values in the female and male lines were 0.243±0.020 and 0.220±0.022, respectively. Importantly, by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS, both GBLUP and GFBLUP resulted in a greater accuracy and smaller bias of genomic prediction. For the startle response, the best accuracy values were 0.258±0.019 (0.237±0.019) for GBLUP and 0.265±0.018 (0.245±0.020) for GFBLUP in the female (male) lines. For starvation resistance, the best accuracy values were 0.437±0.015 (0.427±0.015) for GBLUP and 0.419±0.016 (0.390±0.014) for GFBLUP in female (male) lines. Compared to the GBLUP with complete WGS data, the best accuracy values represented increases of 60.66% and 39.09% for the startle response and 27.40% and 35.36% for starvation resistance in the female and male lines, respectively. Conclusions: Overall, multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction. The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction.

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Section: Genomic Feature Blup Model For Genomic Predictionsupporting

confidence: 88%

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

Zhang

2020

Preprint

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“…grouping genetic variants into functional pathways, can increase the prediction accuracy markedly. [33][34][35][36][37][38] Therefore, we investigated if similar benefits could be achieved by partitioning the metabolome.…”

Section: Nmr Cluster-guided Phenotypic Predictionsmentioning

confidence: 99%

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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Understanding the genotype -phenotype map and how variation at different levels of biological organization are associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins, metabolites. This facilitates our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We do this by performing NMR metabolomics on four replicate pools of male flies from each of 170 isogenic lines. We show first that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, we show that for four of five traits related to behaviour and stress resistance the metabolome predicts phenotypes more accurately than genomic data, and that the metabolites driving the increased accuracy was trait specific. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding has strong applied importance e.g. in human medicine and animal and plant breeding.1 is a design matrix linking the genomic ( 1 ) and metabolomic ( 1 ) effects to the phenotypes. The random genomic effects are 1~( , [1,1] ! " ), and the metabolomic effects are 1~( , [1,1] 5 " ), where is the additive genomic relationship matrix as specified previously, and is the metabolomic relationship matrix.The metabolomic relationship matrix was computed as = ′ NMR ⁄ , where is a × NMR matrix of *77 18* T 6 , N 6 ), and was compared (using paired t-test corrected for multiple testing by a false discovery rate (FDR) of <0.05) within and across 9/ clusters to identify the NMR features resulting in the largest prediction accuracy. We only considered Drosophila melanogaster Genetic Reference Panel lines.

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“…Gene ontology (GO) terms are a useful resource of groups of genes with shared biological, molecular or cellular functions (The Gene Ontology Consortium 2000). An extension of the standard genomic best linear unbiased prediction (GBLUP, Meuwissen et al (2001)), which allows the addition of an extra genetic component for a set of genetic markers, e.g., all those within a certain GO term, is genomic feature best linear unbiased prediction (GFBLUP, Edwards et al (2015, 2016; Ehsani et al (2015); Sarup et al (2016);Fang et al (2017)). The genomic feature model is described below, along with a method to decompose the genetic variance within a GO term to the variance explained per gene within GO term.…”

Section: Identification Of Predictive Snp Setsmentioning

confidence: 99%

“…Previous work has demonstrated that prediction models incorporating prior biological information enriched for causal variants can increase the predictive ability of complex traits (Edwards et al 2016;Sarup et al 2016;Fang et al 2017). Recently it was shown in the DGRP that the level of predictive ability of complex traits depends on the underlying genetic architecture (Edwards et al 2016).…”

Section: Determining the Genotype-phenotype Mapmentioning

confidence: 99%

“…Extending GBLUP by fitting multiple genetic components has been shown to increase predictive ability (Speed and Balding 2014;Tucker et al 2015). In particular, fitting multiple genetic components can be used to partition genetic variance by pathway (Edwards et al 2015), or to test the predictive ability of predefined sets of genes using genomic feature BLUP (GF-BLUP) (Edwards et al 2016;Sarup et al 2016;Fang et al 2017). The second set of genomic analyses therefore included genomic prediction models where the aims were to identify sets of genes predictive of the trait variation, but also to investigate means to predict aggression across social rearing conditions.…”

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confidence: 99%

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Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior

et al. 2017

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Human psychiatric disorders such as schizophrenia, bipolar disorder and attention-deficit/hyper-activity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype by social environmental interaction (GSEI); i.e., variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments which turned to be enriched for genome-wide associated variants.A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice.Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders.

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Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection

Cited by 63 publications

References 62 publications

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

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

Prediction of complex phenotypes using theDrosophilametabolome

Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior

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