Functional annotations of three domestic animal genomes provide vital resources for comparative and agricultural research

Kern, Colin; Wang, Ying; Xu, Xiaoqin; Pan, Zhangyuan; Halstead, Michelle M.; Chanthavixay, Ganrea; Saelao, Perot; Waters, Susan; Xiang, Ruidong; Chamberlain, Amanda J.; Korf, Ian F; Delany, Mary E.; Cheng, Hans H.; Medrano, Juan F.; Eenennaam, Alison L. Van; Tuggle, C. K.; Ernst, Catherine W.; Flicek, Paul; Quon, Gerald; Ross, Pablo J.; Zhou, Huaijun

doi:10.1038/s41467-021-22100-8

Cited by 120 publications

(190 citation statements)

References 79 publications

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“…Genomic selection will be more accurate over time and across breeds if causal variants are included in predictive modelling ( MacLeod et al, 2016 ). It is thought that causal variants are found in functional regions of the genome but until recently ( Villar et al, 2015 ; Fang et al, 2019 ; Kern et al, 2021 ) these were not well annotated in cattle ( Koufariotis et al, 2014 ; Nguyen et al, 2018 ). This study annotated functional regions in six tissues in 2–3 Holstein dairy cows using ChIP-seq for four histone modifications and one transcription factor.…”

Section: Discussionmentioning

confidence: 99%

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Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues

et al. 2021

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Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle.

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Section: Discussionmentioning

confidence: 99%

“…Functional regions have been identified in the bovine genome in liver ( Villar et al, 2015 ), rumen epithelial cells ( Fang et al, 2019 ) and other tissues ( Kern et al, 2021 ). However, functional regions can vary between tissues ( Kellis et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues

et al. 2021

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“…In contrast, Halstead et al (2020), which provided one of the first epigenomic resources for livestock study, identified only around 300 000 regulatory elements in each of the study species, cattle and pig. Similarly, Kern et al (2021) identified ~65 000, ~140 000 and ~150 000 active regulatory regions in chicken, pig and cattle, respectively. Nonetheless, these identified regulatory elements when analysed with a gene expression atlas, GWAS and selection signatures for the species such as that available in cattle (Fang et al 2020;Liu et al 2020) provides a more complete picture of the link between gene regulation and traits.…”

Section: Introductionmentioning

confidence: 92%

Opportunity to improve livestock traits using 3D genomics

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The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genomewide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these highthroughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.

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“…The ATAC-seq assay on these eight tissues from pig and cattle was performed to analyze chromatin accessibility conservation across mammals ( Halstead et al, 2020 ). Furthermore, ChIP-seq (four histone modification marks and CTCF) assays across three species and DNase-seq in chickens were performed to annotate dynamic chromatin states across tissues and species ( Kern et al, 2021 ).…”

Section: Use Of the Samplesmentioning

confidence: 99%

Tissue Resources for the Functional Annotation of Animal Genomes

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In order to generate an atlas of the functional elements driving genome expression in domestic animals, the Functional Annotation of Animal Genome (FAANG) strategy was to sample many tissues from a few animals of different species, sexes, ages, and production stages. This article presents the collection of tissue samples for four species produced by two pilot projects, at INRAE (National Research Institute for Agriculture, Food and Environment) and the University of California, Davis. There were three mammals (cattle, goat, and pig) and one bird (chicken). It describes the metadata characterizing these reference sets (1) for animals with origin and selection history, physiological status, and environmental conditions; (2) for samples with collection site and tissue/cell processing; (3) for quality control; and (4) for storage and further distribution. Three sets are identified: set 1 comprises tissues for which collection can be standardized and for which representative aliquots can be easily distributed (liver, spleen, lung, heart, fat depot, skin, muscle, and peripheral blood mononuclear cells); set 2 comprises tissues requiring special protocols because of their cellular heterogeneity (brain, digestive tract, secretory organs, gonads and gametes, reproductive tract, immune tissues, cartilage); set 3 comprises specific cell preparations (immune cells, tracheal epithelial cells). Dedicated sampling protocols were established and uploaded in https://data.faang.org/protocol/samples. Specificities between mammals and chicken are described when relevant. A total of 73 different tissues or tissue sections were collected, and 21 are common to the four species. Having a common set of tissues will facilitate the transfer of knowledge within and between species and will contribute to decrease animal experimentation. Combining data on the same samples will facilitate data integration. Quality control was performed on some tissues with RNA extraction and RNA quality control. More than 5,000 samples have been stored with unique identifiers, and more than 4,000 were uploaded onto the Biosamples database, provided that standard ontologies were available to describe the sample. Many tissues have already been used to implement FAANG assays, with published results. All samples are available without restriction for further assays. The requesting procedure is described. Members of FAANG are encouraged to apply a range of molecular assays to characterize the functional status of collected samples and share their results, in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.

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Functional annotations of three domestic animal genomes provide vital resources for comparative and agricultural research

Cited by 120 publications

References 79 publications

Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues

Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues

Opportunity to improve livestock traits using 3D genomics

Tissue Resources for the Functional Annotation of Animal Genomes

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