Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle

Yan, Ze; Huang, Hanyu; Freebern, Ellen; Santos, D. R. dos; Dai, Dongmei; Si, Jingfang; Ma, Chengbin; Cao, Jie; Guo, Gang; Liu, George E.; Ma, Li; Fang, Lingzhao; Zhang, Yi

doi:10.1186/s12864-020-06909-z

Cited by 32 publications

(25 citation statements)

References 64 publications

(83 reference statements)

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“…Integrative multi-omics approaches to data integration are now widely used to explore and dissect the genomic architecture and physiological basis of complex traits in domestic livestock, including networkbased methods to integrate functional genomics and GWAS data [86][87][88][89][90][91]. However, to the best of our knowledge, this study is the first that uses network biology to systematically combine transcriptomics data from M. bovis-infected macrophages with GWAS data for M. bovis infection resistance in Fig.…”

Section: Discussionmentioning

confidence: 99%

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

et al. 2021

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Background Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. Results The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively. Conclusions The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

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Section: Discussionmentioning

confidence: 99%

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

et al. 2021

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“…Previous research has supported the role of genomic selection in both reducing HYK and genome-guided management practices [ 54 ]. Genome-wide association studies have identified associations that may provide value as markers of HYK risk [ 55 , 56 , 57 ]. Within the current analysis, the lower genetic resistance (or PTA) for ketosis observed for pHYK cows confirms that the predictive models and genetic evaluations are mutually identifying cows experiencing or more disposed to a case of ketosis.…”

Section: Discussionmentioning

confidence: 99%

Hyperketonemia Predictions Provide an On-Farm Management Tool with Epidemiological Insights

Pralle

Amdall²,

Fourdraine

et al. 2021

Animals

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Prediction of hyperketonemia (HYK), a postpartum metabolic disorder in dairy cows, through use of cow and milk data has allowed for high-throughput detection and monitoring during monthly milk sampling. The objective of this study was to determine associations between predicted HYK (pHYK) and production parameters in a dataset generated from routine milk analysis samples. Data from 240,714 lactations across 335 farms were analyzed with multiple linear regression models to determine HYK status. Data on HYK or disease treatment was not solicited. Consistent with past research, pHYK cows had greater previous lactation dry period length, somatic cell count, and dystocia. Cows identified as pHYK had lower milk yield and protein percent but greater milk fat, specifically greater mixed and preformed fatty acids (FA), and greater somatic cell count (SCC). Differential somatic cell count was greater in second and fourth parity pHYK cows. Culling (60d), days open, and number of artificial inseminations were greater in pHYK cows. Hyperketonemia prevalence decreased linearly in herds with greater rolling herd average milk yield. This research confirms previously identified risk factors and negative outcomes associated with pHYK and highlights novel associations with differential SCC, mixed FA, and preformed FA across farm sizes and production levels.

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“…Integrative multi-omics approaches to data integration are now widely used to explore and dissect the genomic architecture and physiological basis of complex traits in domestic livestock, including network-based methods to integrate functional genomics and GWAS data (Canovas et al 2014;Fang et al 2017;Cai et al 2018;Fang et al 2018;Deng et al 2019;Yan et al 2020). However, to the best of our knowledge, this study is the first that uses network biology to systematically combine transcriptomics data from M. bovis-infected macrophages with GWAS data for M. bovis infection resistance in cattle.…”

Section: Discussionmentioning

confidence: 99%

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

Hall

Mullen

McHugo

et al. 2020

Preprint

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Bovine tuberculosis (bTB), caused by infection with Mycobacterium bovis, is a major disease affecting cattle globally as well as being a zoonotic risk to human health. The key innate immune cell that first encounters M. bovis is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use multi-omics and network biology approaches to analyse the macrophage transcriptional response to M. bovis infection and identify core infection response pathways and gene modules. These outputs were integrated with results from genome-wide associations of M. bovis infection to enhance the detection of putative genomic variants for disease resistance. Our results show that network-based integration of relevant transcriptomics data can extract additional information from large genome-wide associations and that this approach could also be used to integrate relevant functional genomics outputs with results from genomic association studies for human tuberculosis caused by the related Mycobacterium tuberculosis.

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Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle

Cited by 32 publications

References 64 publications

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

Hyperketonemia Predictions Provide an On-Farm Management Tool with Epidemiological Insights

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

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