1
|
Ko H, Pasternak JA, Mulligan MK, Hamonic G, Ramesh N, MacPhee DJ, Plastow GS, Harding JCS. A DIO2 missense mutation and its impact on fetal response to PRRSV infection. BMC Vet Res 2024; 20:255. [PMID: 38867209 PMCID: PMC11167750 DOI: 10.1186/s12917-024-04099-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/27/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Porcine reproductive and respiratory syndrome virus 2 (PRRSV-2) infection during late gestation substantially lowers fetal viability and survival. In a previous genome-wide association study, a single nucleotide polymorphism on chromosome 7 was significantly associated with probability of fetuses being viable in response to maternal PRRSV-2 infection at 21 days post maternal inoculation. The iodothyronine deiodinase 2 (DIO2) gene, located ~ 14 Kilobase downstream of this SNP, was selected as a priority candidate related to fetal susceptibility following maternal PRRSV-2 infection. Our objectives were to identify mutation(s) within the porcine DIO2 gene and to determine if they were associated with fetal outcomes after PRRSV-2 challenge. Sequencing of the DIO2, genotyping identified variants, and association of DIO2 genotypes with fetal phenotypes including DIO2 mRNA levels, viability, survival, viral loads, cortisol and thyroid hormone levels, and growth measurements were conducted. RESULTS A missense variant (p.Asn91Ser) was identified in the parental populations from two independent PRRSV-2 challenge trials. This variant was further genotyped to determine association with fetal PRRS outcomes. DIO2 mRNA levels in fetal heart and kidney differed by the genotypes of Asn91Ser substitution with significantly greater DIO2 mRNA expression in heterozygotes compared with wild-type homozygotes (P < 0.001 for heart, P = 0.002 for kidney). While Asn91Ser did not significantly alter fetal viability and growth measurements, interaction effects of the variant with fetal sex or trial were identified for fetal viability or crown rump length, respectively. However, this mutation was not related to dysregulation of the hypothalamic-pituitary-adrenal and thyroid axis, indicated by no differences in circulating cortisol, T4, and T3 levels in fetuses of the opposing genotypes following PRRSV-2 infection. CONCLUSIONS The present study suggests that a complex relationship among DIO2 genotype, DIO2 expression, fetal sex, and fetal viability may exist during the course of fetal PRRSV infection. Our study also proposes the increase in cortisol levels, indicative of fetal stress response, may lead to fetal complications, such as fetal compromise, fetal death, or premature farrowing, during PRRSV infection.
Collapse
Affiliation(s)
- Haesu Ko
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, S7N5B4, Canada
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G2H1, Canada
| | - J Alex Pasternak
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Margaret K Mulligan
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Glenn Hamonic
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, S7N5B4, Canada
| | - Naresh Ramesh
- Department of Biology, West Virginia University Institute of Technology, Beckley, WV, 25801, USA
| | - Daniel J MacPhee
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, S7N5B4, Canada
| | - Graham S Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G2H1, Canada
| | - John C S Harding
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, S7N5B4, Canada.
| |
Collapse
|
2
|
Tang Y, Zhang J, Li W, Liu X, Chen S, Mi S, Yang J, Teng J, Fang L, Yu Y. Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle. BMC Genomics 2024; 25:445. [PMID: 38711039 DOI: 10.1186/s12864-024-10346-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 04/25/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22-150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle. RESULTS We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits. CONCLUSIONS This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.
Collapse
Affiliation(s)
- Yongjie Tang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jinning Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wenlong Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Xueqin Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Siqian Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Siyuan Mi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jinyan Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, 8000, Denmark.
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
| |
Collapse
|
3
|
Zeng Q, Du ZQ. Advances in the discovery of genetic elements underlying longissimus dorsi muscle growth and development in the pig. Anim Genet 2023; 54:709-720. [PMID: 37796678 DOI: 10.1111/age.13365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/08/2023] [Accepted: 07/08/2023] [Indexed: 10/07/2023]
Abstract
As a major source of protein in human diets, pig meat plays a crucial role in ensuring global food security. Key determinants of meat production refer to the chemical and physical compositions or characteristics of muscle fibers, such as the number, hypertrophy potential, fiber-type conversion and intramuscular fat deposition. However, the growth and formation of muscle fibers comprises a complex process under spatio-temporal regulation, that is, the intermingled and concomitant proliferation, differentiation, migration and fusion of myoblasts. Recently, with the fast and continuous development of next-generation sequencing technology, the integration of quantitative trait loci mapping with genome-wide association studies (GWAS) has greatly helped animal geneticists to discover and explore thousands of functional or causal genetic elements underlying muscle growth and development. However, owing to the underlying complex molecular mechanisms, challenges to in-depth understanding and utilization remain, and the cost of large-scale sequencing, which requires integrated analyses of high-throughput omics data, is high. In this review, we mainly elaborate on research advances in integrative analyses (e.g. GWAS, omics) for identifying functional genes or genomic elements for longissimus dorsi muscle growth and development for different pig breeds, describing several successful transcriptome analyses and functional genomics cases, in an attempt to provide some perspective on the future functional annotation of genetic elements for muscle growth and development in pigs.
Collapse
Affiliation(s)
- Qingjie Zeng
- College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Zhi-Qiang Du
- College of Animal Science, Yangtze University, Jingzhou, Hubei, China
| |
Collapse
|
4
|
Peka M, Balatsky V, Saienko A, Tsereniuk O. Bioinformatic analysis of the effect of SNPs in the pig TERT gene on the structural and functional characteristics of the enzyme to develop new genetic markers of productivity traits. BMC Genomics 2023; 24:487. [PMID: 37626279 PMCID: PMC10463782 DOI: 10.1186/s12864-023-09592-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Telomerase reverse transcriptase (TERT) plays a crucial role in synthesizing telomeric repeats that safeguard chromosomes from damage and fusion, thereby maintaining genome stability. Mutations in the TERT gene can lead to a deviation in gene expression, impaired enzyme activity, and, as a result, abnormal telomere shortening. Genetic markers of productivity traits in livestock can be developed based on the TERT gene polymorphism for use in marker-associated selection (MAS). In this study, a bioinformatic-based approach is proposed to evaluate the effect of missense single-nucleotide polymorphisms (SNPs) in the pig TERT gene on enzyme function and structure, with the prospect of developing genetic markers. RESULTS A comparative analysis of the coding and amino acid sequences of the pig TERT was performed with corresponding sequences of other species. The distribution of polymorphisms in the pig TERT gene, with respect to the enzyme's structural-functional domains, was established. A three-dimensional model of the pig TERT structure was obtained through homological modeling. The potential impact of each of the 23 missense SNPs in the pig TERT gene on telomerase function and stability was assessed using predictive bioinformatic tools utilizing data on the amino acid sequence and structure of pig TERT. CONCLUSIONS According to bioinformatic analysis of 23 missense SNPs of the pig TERT gene, a predictive effect of rs789641834 (TEN domain), rs706045634 (TEN domain), rs325294961 (TRBD domain) and rs705602819 (RTD domain) on the structural and functional parameters of the enzyme was established. These SNPs hold the potential to serve as genetic markers of productivity traits. Therefore, the possibility of their application in MAS should be further evaluated in associative analysis studies.
Collapse
Affiliation(s)
- Mykyta Peka
- Institute of Pig Breeding and Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, 1 Shvedska Mohyla St, Poltava, 36013 Ukraine
- V. N. Karazin Kharkiv National University, 4 Svobody Sq, Kharkiv, 61022 Ukraine
| | - Viktor Balatsky
- Institute of Pig Breeding and Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, 1 Shvedska Mohyla St, Poltava, 36013 Ukraine
- V. N. Karazin Kharkiv National University, 4 Svobody Sq, Kharkiv, 61022 Ukraine
| | - Artem Saienko
- Institute of Pig Breeding and Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, 1 Shvedska Mohyla St, Poltava, 36013 Ukraine
| | - Oleksandr Tsereniuk
- Institute of Pig Breeding and Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, 1 Shvedska Mohyla St, Poltava, 36013 Ukraine
| |
Collapse
|
5
|
Desire S, Johnsson M, Ros-Freixedes R, Chen CY, Holl JW, Herring WO, Gorjanc G, Mellanby RJ, Hickey JM, Jungnickel MK. A genome-wide association study for loin depth and muscle pH in pigs from intensely selected purebred lines. Genet Sel Evol 2023; 55:42. [PMID: 37322449 DOI: 10.1186/s12711-023-00815-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 05/26/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) aim at identifying genomic regions involved in phenotype expression, but identifying causative variants is difficult. Pig Combined Annotation Dependent Depletion (pCADD) scores provide a measure of the predicted consequences of genetic variants. Incorporating pCADD into the GWAS pipeline may help their identification. Our objective was to identify genomic regions associated with loin depth and muscle pH, and identify regions of interest for fine-mapping and further experimental work. Genotypes for ~ 40,000 single nucleotide morphisms (SNPs) were used to perform GWAS for these two traits, using de-regressed breeding values (dEBV) for 329,964 pigs from four commercial lines. Imputed sequence data was used to identify SNPs in strong ([Formula: see text] 0.80) linkage disequilibrium with lead GWAS SNPs with the highest pCADD scores. RESULTS Fifteen distinct regions were associated with loin depth and one with loin pH at genome-wide significance. Regions on chromosomes 1, 2, 5, 7, and 16, explained between 0.06 and 3.55% of the additive genetic variance and were strongly associated with loin depth. Only a small part of the additive genetic variance in muscle pH was attributed to SNPs. The results of our pCADD analysis suggests that high-scoring pCADD variants are enriched for missense mutations. Two close but distinct regions on SSC1 were associated with loin depth, and pCADD identified the previously identified missense variant within the MC4R gene for one of the lines. For loin pH, pCADD identified a synonymous variant in the RNF25 gene (SSC15) as the most likely candidate for the muscle pH association. The missense mutation in the PRKAG3 gene known to affect glycogen content was not prioritised by pCADD for loin pH. CONCLUSIONS For loin depth, we identified several strong candidate regions for further statistical fine-mapping that are supported in the literature, and two novel regions. For loin muscle pH, we identified one previously identified associated region. We found mixed evidence for the utility of pCADD as an extension of heuristic fine-mapping. The next step is to perform more sophisticated fine-mapping and expression quantitative trait loci (eQTL) analysis, and then interrogate candidate variants in vitro by perturbation-CRISPR assays.
Collapse
Affiliation(s)
- Suzanne Desire
- The Roslin Institute, The University of Edinburgh, Midlothian, UK.
| | - Martin Johnsson
- Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Roger Ros-Freixedes
- Departament de Ciència Animal, Universitat de Lleida-Agrotecnio-CERCA Center, Lleida, Spain
| | - Ching-Yi Chen
- The Pig Improvement Company, Genus Plc, Hendersonville, TN, USA
| | - Justin W Holl
- The Pig Improvement Company, Genus Plc, Hendersonville, TN, USA
| | | | - Gregor Gorjanc
- The Roslin Institute, The University of Edinburgh, Midlothian, UK
| | - Richard J Mellanby
- The Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - John M Hickey
- The Roslin Institute, The University of Edinburgh, Midlothian, UK
| | | |
Collapse
|
6
|
Melzer N, Qanbari S, Ding X, Wittenburg D. CLARITY: a Shiny app for interactive visualisation of the bovine physical-genetic map. Front Genet 2023; 14:1082782. [PMID: 37323679 PMCID: PMC10267868 DOI: 10.3389/fgene.2023.1082782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Abstract
The arrangement of markers on the genome can be defined in either physical or linkage terms. While a physical map represents the inter-marker distances in base pairs, a genetic (or linkage) map pictures the recombination rate between pairs of markers. High-resolution genetic maps are key elements for genomic research, such as fine-mapping of quantitative trait loci, but they are also needed for creating and updating chromosome-level assemblies of whole-genome sequences. Based on published results on a large pedigree of German Holstein cattle and newly obtained results with German/Austrian Fleckvieh cattle, we aim at providing a platform that allows users to interactively explore the bovine genetic and physical map. We developed the R Shiny app CLARITY available online at https://nmelzer.shinyapps.io/clarity and as R package at https://github.com/nmelzer/CLARITY that provides access to the genetic maps built on the Illumina Bovine SNP50 genotyping array with markers ordered according to the physical coordinates of the most recent bovine genome assembly ARS-UCD1.2. The user is able to interconnect the physical and genetic map for a whole chromosome or a specific chromosomal region and can inspect a landscape of recombination hotspots. Moreover, the user can investigate which of the frequently used genetic-map functions locally fits best. We further provide auxiliary information about markers being putatively misplaced in the ARS-UCD1.2 release. The corresponding output tables and figures can be downloaded in various formats. By ongoing data integration from different breeds, the app also facilitates comparison of different genome features, providing a valuable tool for education and research purposes.
Collapse
|
7
|
Johnsson M. Genomics in animal breeding from the perspectives of matrices and molecules. Hereditas 2023; 160:20. [PMID: 37149663 PMCID: PMC10163706 DOI: 10.1186/s41065-023-00285-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/03/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND This paper describes genomics from two perspectives that are in use in animal breeding and genetics: a statistical perspective concentrating on models for estimating breeding values, and a sequence perspective concentrating on the function of DNA molecules. MAIN BODY This paper reviews the development of genomics in animal breeding and speculates on its future from these two perspectives. From the statistical perspective, genomic data are large sets of markers of ancestry; animal breeding makes use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; what animal breeding needs is to identify and make use of them. CONCLUSION The statistical perspective, in the form of genomic selection, is the more applicable in contemporary breeding. Animal genomics researchers using from the sequence perspective are still working towards this the isolation of causative variants, equipped with new technologies but continuing a decades-long line of research.
Collapse
Affiliation(s)
- Martin Johnsson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, Uppsala, 75007, Sweden.
| |
Collapse
|
8
|
Nosková A, Mehrotra A, Kadri NK, Lloret-Villas A, Neuenschwander S, Hofer A, Pausch H. Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs. BMC Genomics 2023; 24:192. [PMID: 37038103 PMCID: PMC10084639 DOI: 10.1186/s12864-023-09295-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/04/2023] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL). RESULTS We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants. CONCLUSIONS Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.
Collapse
Affiliation(s)
- A Nosková
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland.
| | - A Mehrotra
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | - N K Kadri
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | | | | | - A Hofer
- SUISAG, Allmend 10, 6204, Sempach, Switzerland
| | - H Pausch
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| |
Collapse
|
9
|
Ros-Freixedes R, Johnsson M, Whalen A, Chen CY, Valente BD, Herring WO, Gorjanc G, Hickey JM. Genomic prediction with whole-genome sequence data in intensely selected pig lines. GENETICS SELECTION EVOLUTION 2022; 54:65. [PMID: 36153511 PMCID: PMC9509613 DOI: 10.1186/s12711-022-00756-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 09/05/2022] [Indexed: 12/03/2022]
Abstract
Background Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. Methods We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. Results The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. Conclusions Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-022-00756-0.
Collapse
|
10
|
Li J, Xiang Y, Zhang L, Qi X, Zheng Z, Zhou P, Tang Z, Jin Y, Zhao Q, Fu Y, Zhao Y, Li X, Fu L, Zhao S. Enhancer-promoter interaction maps provide insights into skeletal muscle-related traits in pig genome. BMC Biol 2022; 20:136. [PMID: 35681201 PMCID: PMC9185926 DOI: 10.1186/s12915-022-01322-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/06/2022] [Indexed: 12/03/2022] Open
Abstract
Background Gene expression programs are intimately linked to the interplay of active cis regulatory elements mediated by chromatin contacts and associated RNAs. Genome-wide association studies (GWAS) have identified many variants in these regulatory elements that can contribute to phenotypic diversity. However, the functional interpretation of these variants remains nontrivial due to the lack of chromatin contact information or limited contact resolution. Furthermore, the distribution and role of chromatin-associated RNAs in gene expression and chromatin conformation remain poorly understood. To address this, we first present a comprehensive interaction map of nuclear dynamics of 3D chromatin-chromatin interactions (H3K27ac BL-HiChIP) and RNA-chromatin interactions (GRID-seq) to reveal genomic variants that contribute to complex skeletal muscle traits. Results In a genome-wide scan, we provide systematic fine mapping and gene prioritization from GWAS leading signals that underlie phenotypic variability of growth rate, meat quality, and carcass performance. A set of candidate functional variants and 54 target genes previously not detected were identified, with 71% of these candidate functional variants choosing to skip over their nearest gene to regulate the target gene in a long-range manner. The effects of three functional variants regulating KLF6 (related to days to 100 kg), MXRA8 (related to lean meat percentage), and TAF11 (related to loin muscle depth) were observed in two pig populations. Moreover, we find that this multi-omics interaction map consists of functional communities that are enriched in specific biological functions, and GWAS target genes can serve as core genes for exploring peripheral trait-relevant genes. Conclusions Our results provide a valuable resource of candidate functional variants for complex skeletal muscle-related traits and establish an integrated approach to complement existing 3D genomics by exploiting RNA-chromatin and chromatin-chromatin interactions for future association studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01322-2.
Collapse
Affiliation(s)
- Jingjin Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yue Xiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Lu Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Xiaolong Qi
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Zhuqing Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Peng Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Zhenshuang Tang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yi Jin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Qiulin Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yuhua Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yunxia Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,Hubei Hongshan Laboratory, 430070, Wuhan, People's Republic of China.
| | - Liangliang Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,Hubei Hongshan Laboratory, 430070, Wuhan, People's Republic of China.
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,Hubei Hongshan Laboratory, 430070, Wuhan, People's Republic of China.
| |
Collapse
|
11
|
Dauben CM, Große-Brinkhaus C, Heuß EM, Henne H, Tholen E. Comparison of the choice of animals for re-sequencing in two maternal pig lines. Genet Sel Evol 2022; 54:16. [PMID: 35183111 PMCID: PMC8858453 DOI: 10.1186/s12711-022-00706-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 01/31/2022] [Indexed: 11/10/2022] Open
Abstract
Next-generation sequencing is a promising approach for the detection of causal variants within previously identified quantitative trait loci. Because of the costs of re-sequencing experiments, this application is currently mainly restricted to subsets of animals from already genotyped populations. Imputation from a lower to a higher marker density could represent a useful complementary approach. An analysis of the literature shows that several strategies are available to select animals for re-sequencing. This study demonstrates an animal selection workflow under practical conditions. Our approach considers different data sources and limited resources such as budget and availability of sampling material. The workflow combines previously described approaches and makes use of genotype and pedigree information from a Landrace and Large White population. Genotypes were phased and haplotypes were accurately estimated with AlphaPhase. Then, AlphaSeqOpt was used to optimize selection of animals for re-sequencing, reflecting the existing diversity of haplotypes. AlphaSeqOpt and ENDOG were used to select individuals based on pedigree information and by taking into account key animals that represent the genetic diversity of the populations. After the best selection criteria were determined, a subset of 57 animals was selected for subsequent re-sequencing. In order to evaluate and assess the advantage of this procedure, imputation accuracy was assessed by setting a set of single nucleotide polymorphism (SNP) chip genotypes to missing. Accuracy values were compared to those of alternative selection scenarios and the results showed the clear benefits of a targeted selection within this practical-driven approach. Especially imputation of low-frequency markers benefits from the combined approach described here. Accuracy was increased by up to 12% compared to a randomized or exclusively haplotype-based selection of sequencing candidates.
Collapse
Affiliation(s)
- Christina M Dauben
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| | | | - Esther M Heuß
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| | - Hubert Henne
- BHZP GmbH, An der Wassermühle 8, 21368, Dahlenburg-Ellringen, Germany
| | - Ernst Tholen
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| |
Collapse
|