1
|
Han P, Zhang W, Wang D, Wu Y, Li X, Zhao S, Zhu M. Comparative transcriptome analysis of T lymphocyte subpopulations and identification of critical regulators defining porcine thymocyte identity. Front Immunol 2024; 15:1339787. [PMID: 38384475 PMCID: PMC10879363 DOI: 10.3389/fimmu.2024.1339787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction The development and migration of T cells in the thymus and peripheral tissues are crucial for maintaining adaptive immunity in mammals. However, the regulatory mechanisms underlying T cell development and thymocyte identity formation in pigs remain largely underexplored. Method Here, by integrating bulk and single-cell RNA-sequencing data, we investigated regulatory signatures of porcine thymus and lymph node T cells. Results The comparison of T cell subpopulations derived from porcine thymus and lymph nodes revealed that their transcriptomic differences were influenced more by tissue origin than by T cell phenotypes, and that lymph node cells exhibited greater transcriptional diversity than thymocytes. Through weighted gene co-expression network analysis (WGCNA), we identified the key modules and candidate hub genes regulating the heterogeneity of T cell subpopulations. Further, we integrated the porcine thymocyte dataset with peripheral blood mononuclear cell (PBMC) dataset to systematically compare transcriptomic differences between T cell types from different tissues. Based on single-cell datasets, we further identified the key transcription factors (TFs) responsible for maintaining porcine thymocyte identity and unveiled that these TFs coordinately regulated the entire T cell development process. Finally, we performed GWAS of cell type-specific differentially expressed genes (DEGs) and 30 complex traits, and found that the DEGs in thymus-related and peripheral blood-related cell types, especially CD4_SP cluster and CD8-related cluster, were significantly associated with pig productive and reproductive traits. Discussion Our findings provide an insight into T cell development and lay a foundation for further exploring the porcine immune system and genetic mechanisms underlying complex traits in pigs.
Collapse
Affiliation(s)
- Pingping Han
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Wei Zhang
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Daoyuan Wang
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Yalan Wu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Xinyun Li
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
| | - Mengjin Zhu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
| |
Collapse
|
2
|
Rohde PD, Fourie Sørensen I, Sørensen P. Expanded utility of the R package, qgg, with applications within genomic medicine. Bioinformatics 2023; 39:btad656. [PMID: 37882742 PMCID: PMC10627350 DOI: 10.1093/bioinformatics/btad656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 09/17/2023] [Accepted: 10/24/2023] [Indexed: 10/27/2023] Open
Abstract
SUMMARY Here, we present an expanded utility of the R package qgg for genetic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes Bayesian linear regression modeling procedures, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study summary data. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software and data formats. AVAILABILITY AND IMPLEMENTATION The methodologies are implemented in the publicly available R software package, qgg, using fast and memory efficient algorithms in C++ and is available on CRAN or as a developer version at our GitHub page (https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available at our GitHub page https://psoerensen.github.io/qgg/.
Collapse
Affiliation(s)
- Palle Duun Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, 9260 Gistrup, Denmark
| | - Izel Fourie Sørensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark
| |
Collapse
|
3
|
Therkildsen J, Rohde PD, Nissen L, Thygesen J, Hauge EM, Langdahl BL, Boettcher M, Nyegaard M, Winther S. A genome-wide genomic score added to standard recommended stratification tools does not improve the identification of patients with very low bone mineral density. Osteoporos Int 2023; 34:1893-1906. [PMID: 37495683 PMCID: PMC10579117 DOI: 10.1007/s00198-023-06857-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/10/2023] [Indexed: 07/28/2023]
Abstract
The role of integrating genomic scores (GSs) needs to be assessed. Adding a GS to recommended stratification tools does not improve the prediction of very low bone mineral density. However, we noticed that the GS performed equally or above individual risk factors in discrimination. PURPOSE We aimed to investigate whether adding a genomic score (GS) to recommended stratification tools improves the discrimination of participants with very low bone mineral density (BMD). METHODS BMD was measured in three thoracic vertebrae using CT. All participants provided information on standard osteoporosis risk factors. GSs and FRAX scores were calculated. Participants were grouped according to mean BMD into very low (<80 mg/cm3), low (80-120 mg/cm3), and normal (>120 mg/cm3) and according to the Bone Health and Osteoporosis Foundation recommendations for BMD testing into an "indication for BMD testing" and "no indication for BMD testing" group. Different models were assessed using the area under the receiver operating characteristics curves (AUC) and reclassification analyses. RESULTS In the total cohort (n=1421), the AUC for the GS was 0.57 (95% CI 0.52-0.61) corresponding to AUCs for osteoporosis risk factors. In participants without indication for BMD testing, the AUC was 0.60 (95% CI 0.52-0.69) above or equal to AUCs for osteoporosis risk factors. Adding the GS to a clinical risk factor (CRF) model resulted in AUCs not statistically significant from the CRF model. Using probability cutoff values of 6, 12, and 24%, we found no improved reclassification or risk discrimination using the CRF-GS model compared to the CRF model. CONCLUSION Our results suggest adding a GS to a CRF model does not improve prediction. However, we noticed that the GS performed equally or above individual risk factors in discrimination. Clinical risk factors combined showed superior discrimination to individual risk factors and the GS, underlining the value of combined CRFs in routine clinics as a stratification tool.
Collapse
Affiliation(s)
- J Therkildsen
- Department of Rheumatology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus, Denmark.
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark.
| | - P D Rohde
- Department of Health Science & Technology, Aalborg University, Selma Lagerløfs Vej 24, 9269, Gistrup, Denmark
| | - L Nissen
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark
- Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, 7400, Herning, Denmark
| | - J Thygesen
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark
- Department of Clinical Engineering, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus, Denmark
| | - E-M Hauge
- Department of Rheumatology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark
| | - B L Langdahl
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus, Denmark
| | - M Boettcher
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark
- Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, 7400, Herning, Denmark
| | - M Nyegaard
- Department of Health Science & Technology, Aalborg University, Selma Lagerløfs Vej 24, 9269, Gistrup, Denmark
- Department of Biomedicine, Aarhus University, Høegh-Guldbergs Gade 10, 8000, Aarhus, Denmark
| | - S Winther
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark
- Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, 7400, Herning, Denmark
| |
Collapse
|
4
|
Møller PL, Rohde PD, Dahl JN, Rasmussen LD, Schmidt SE, Nissen L, McGilligan V, Bentzon JF, Gudbjartsson DF, Stefansson K, Holm H, Winther S, Bøttcher M, Nyegaard M. Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:442-451. [PMID: 37753640 DOI: 10.1161/circgen.123.004053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/11/2023] [Indexed: 09/28/2023]
Abstract
BACKGROUND Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS). METHODS Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning. RESULTS Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients. CONCLUSIONS For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION URL: https://www. CLINICALTRIALS gov; Unique identifier: NCT02264717.
Collapse
Affiliation(s)
- Peter Loof Møller
- Department of Biomedicine (P.L.M., M.N.), Aarhus University
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
| | - Palle Duun Rohde
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
| | - Jonathan Nørtoft Dahl
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Laust Dupont Rasmussen
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Samuel Emil Schmidt
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
| | - Louise Nissen
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Victoria McGilligan
- Personalized Medicine Centre, Ulster University, Derry, Northern Ireland (V.M.)
| | - Jacob F Bentzon
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain (J.F.B.)
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc. (D.F.G., K.S., H.H.)
- School of Engineering and Natural Sciences (D.F.G.)
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc. (D.F.G., K.S., H.H.)
- Faculty of Medicine, University of Iceland, Reykjavik (K.S.)
| | - Hilma Holm
- deCODE genetics/Amgen, Inc. (D.F.G., K.S., H.H.)
| | - Simon Winther
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Morten Bøttcher
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Mette Nyegaard
- Department of Biomedicine (P.L.M., M.N.), Aarhus University
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
| |
Collapse
|
5
|
Wu Y, Li D, Hu Y, Li H, Ramstein GP, Zhou S, Zhang X, Bao Z, Zhang Y, Song B, Zhou Y, Zhou Y, Gagnon E, Särkinen T, Knapp S, Zhang C, Städler T, Buckler ES, Huang S. Phylogenomic discovery of deleterious mutations facilitates hybrid potato breeding. Cell 2023; 186:2313-2328.e15. [PMID: 37146612 DOI: 10.1016/j.cell.2023.04.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/20/2023] [Accepted: 04/05/2023] [Indexed: 05/07/2023]
Abstract
Hybrid potato breeding will transform the crop from a clonally propagated tetraploid to a seed-reproducing diploid. Historical accumulation of deleterious mutations in potato genomes has hindered the development of elite inbred lines and hybrids. Utilizing a whole-genome phylogeny of 92 Solanaceae and its sister clade species, we employ an evolutionary strategy to identify deleterious mutations. The deep phylogeny reveals the genome-wide landscape of highly constrained sites, comprising ∼2.4% of the genome. Based on a diploid potato diversity panel, we infer 367,499 deleterious variants, of which 50% occur at non-coding and 15% at synonymous sites. Counterintuitively, diploid lines with relatively high homozygous deleterious burden can be better starting material for inbred-line development, despite showing less vigorous growth. Inclusion of inferred deleterious mutations increases genomic-prediction accuracy for yield by 24.7%. Our study generates insights into the genome-wide incidence and properties of deleterious mutations and their far-reaching consequences for breeding.
Collapse
Affiliation(s)
- Yaoyao Wu
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China; Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Dawei Li
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China; State Key Laboratory of Tropical Crop Breeding, Chinese Academy of Tropical Agricultural Sciences, Haikou, Hainan 571101, China
| | - Yong Hu
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China; The AGISCAAS-YNNU Joint Academy of Potato Sciences, Yunnan Normal University, Kunming, Yunnan 650500, China
| | - Hongbo Li
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China
| | - Guillaume P Ramstein
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus 8000, Denmark
| | - Shaoqun Zhou
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China
| | - Xinyan Zhang
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China
| | - Zhigui Bao
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China; Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
| | - Yu Zhang
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China; School of Agriculture, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Baoxing Song
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261000, China
| | - Yao Zhou
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China; Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100094, China
| | - Yongfeng Zhou
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China
| | - Edeline Gagnon
- Technische Universität München, TUM School of Life Sciences, Emil-Ramann-Strasse 2, 85354 Freising, Germany
| | - Tiina Särkinen
- Royal Botanic Garden Edinburgh, 20A Inverleith Row, Edinburgh EH3 5LR, UK
| | - Sandra Knapp
- Natural History Museum, Cromwell Road, London SW7 5BD, UK
| | - Chunzhi Zhang
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China
| | - Thomas Städler
- Institute of Integrative Biology and Zurich-Basel Plant Science Center, ETH Zurich, 8092 Zurich, Switzerland
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA; USDA-ARS, Ithaca, NY 14853, USA
| | - Sanwen Huang
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China; State Key Laboratory of Tropical Crop Breeding, Chinese Academy of Tropical Agricultural Sciences, Haikou, Hainan 571101, China.
| |
Collapse
|
6
|
Lan Z, Zhang K, He J, Kang Q, Meng W, Wang S. Pectolinarigenin shows lipid-lowering effects by inhibiting fatty acid biosynthesis in vitro and in vivo. Phytother Res 2023; 37:913-925. [PMID: 36415143 DOI: 10.1002/ptr.7679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/21/2022] [Accepted: 11/06/2022] [Indexed: 11/24/2022]
Abstract
Pectolinarigenin is the main flavonoid compound and presents in Linaria vulgaris and Cirsium chanroenicum. In this study, RNA sequencing (RNA-seq) was applied to dissect the effect of pectolinarigenin on the transcriptome changes in the high lipid Huh-7 cells induced by oleic acid. RNA-seq results revealed that 15 pathways enriched by downregulated genes are associated with cell metabolism including cholesterol metabolism, glycerophospholipid metabolism, steroid biosynthesis, steroid hormone biosynthesis, fatty acid biosynthesis, etc. Moreover, 13 key genes related to lipid metabolism were selected. Among them, PPARG coactivator 1 beta (PPARGC1B) and carnitine palmitoyltransferase 1A (CPT1A) were found to be upregulated, solute carrier family 27 member 1(SLC27A1), acetyl-CoA carboxylase alpha (ACACA), fatty-acid synthase (FASN), 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR), etc. were found to be downregulated. Glycolysis/gluconeogenesis, steroid hormone biosynthesis, and fatty acid biosynthesis were all significantly downregulated, according to gene set variation analysis and gene set enrichment analysis. Besides, protein levels of FASN, ACACA, and SLC27A1 were all decreased, whereas PPARγ and CPT1A were increased. Docking models showed that PPARγ may be a target for pectolinarigenin. Furthermore, pectolinarigenin reduced serum TG and hepatic TG, and improved insulin sensitivity in vivo. Our findings suggest that pectolinarigenin may target PPARγ and prevent fatty acid biosynthesis.
Collapse
Affiliation(s)
- Zhou Lan
- Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Kun Zhang
- Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Jianhui He
- Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Qiong Kang
- Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Wei Meng
- Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Songhua Wang
- Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| |
Collapse
|
7
|
Shi L, Wang L, Fang L, Li M, Tian J, Wang L, Zhao F. Integrating genome-wide association studies and population genomics analysis reveals the genetic architecture of growth and backfat traits in pigs. Front Genet 2022; 13:1078696. [PMID: 36506319 PMCID: PMC9732542 DOI: 10.3389/fgene.2022.1078696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/14/2022] [Indexed: 11/26/2022] Open
Abstract
Growth and fat deposition are complex traits, which can affect economical income in the pig industry. Due to the intensive artificial selection, a significant genetic improvement has been observed for growth and fat deposition in pigs. Here, we first investigated genomic-wide association studies (GWAS) and population genomics (e.g., selection signature) to explore the genetic basis of such complex traits in two Large White pig lines (n = 3,727) with the GeneSeek GGP Porcine HD array (n = 50,915 SNPs). Ten genetic variants were identified to be associated with growth and fatness traits in two Large White pig lines from different genetic backgrounds by performing both within-population GWAS and cross-population GWAS analyses. These ten significant loci represented eight candidate genes, i.e., NRG4, BATF3, IRS2, ANO1, ANO9, RNF152, KCNQ5, and EYA2. One of them, ANO1 gene was simultaneously identified for both two lines in BF100 trait. Compared to single-population GWAS, cross-population GWAS was less effective for identifying SNPs with population-specific effect, but more powerful for detecting SNPs with population-shared effects. We further detected genomic regions specifically selected in each of two populations, but did not observe a significant enrichment for the heritability of growth and backfat traits in such regions. In summary, the candidate genes will provide an insight into the understanding of the genetic architecture of growth-related traits and backfat thickness, and may have a potential use in the genomic breeding programs in pigs.
Collapse
Affiliation(s)
- Liangyu Shi
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,Laboratory of Genetic Breeding, Reproduction and Precision Livestock Farming, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Ligang Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mianyan Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jingjing Tian
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lixian Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,*Correspondence: Lixian Wang, ; Fuping Zhao,
| | - Fuping Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,*Correspondence: Lixian Wang, ; Fuping Zhao,
| |
Collapse
|
8
|
Ramstein GP, Buckler ES. Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize. Genome Biol 2022; 23:183. [PMID: 36050782 PMCID: PMC9438327 DOI: 10.1186/s13059-022-02747-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 08/15/2022] [Indexed: 11/10/2022] Open
Abstract
Background Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations. Results Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants. Conclusions Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (10.25739/hybz-2957). Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02747-2.
Collapse
Affiliation(s)
- Guillaume P Ramstein
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark. .,Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA.
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA.,USDA-ARS, Ithaca, NY, 14853, USA
| |
Collapse
|
9
|
Yao Y, Liu S, Xia C, Gao Y, Pan Z, Canela-Xandri O, Khamseh A, Rawlik K, Wang S, Li B, Zhang Y, Pairo-Castineira E, D’Mellow K, Li X, Yan Z, Li CJ, Yu Y, Zhang S, Ma L, Cole JB, Ross PJ, Zhou H, Haley C, Liu GE, Fang L, Tenesa A. Comparative transcriptome in large-scale human and cattle populations. Genome Biol 2022; 23:176. [PMID: 35996157 PMCID: PMC9394047 DOI: 10.1186/s13059-022-02745-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 08/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes. RESULTS Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues. CONCLUSIONS In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.
Collapse
Affiliation(s)
- Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Charley Xia
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
- Department of Psychology, 7 George Square, The University of Edinburgh, Edinburgh, EH8 9JZ UK
| | - Yahui Gao
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MA 20742 USA
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, CA 95616 USA
- Present address: Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Ava Khamseh
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223 Yunnan China
| | - Bingjie Li
- Scotland’s Rural College (SRUC), Roslin Institute Building, Midlothian, EH25 9RG UK
| | - Yi Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Erola Pairo-Castineira
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - Kenton D’Mellow
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225 Guangdong China
| | - Ze Yan
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Cong-jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MA 20742 USA
| | - John B. Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Pablo J. Ross
- Department of Animal Science, University of California, Davis, CA 95616 USA
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616 USA
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- Present address: Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| |
Collapse
|
10
|
Guo X, Jahoor A, Jensen J, Sarup P. Metabolomic spectra for phenotypic prediction of malting quality in spring barley. Sci Rep 2022; 12:7881. [PMID: 35551263 PMCID: PMC9098465 DOI: 10.1038/s41598-022-12028-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 05/04/2022] [Indexed: 11/16/2022] Open
Abstract
We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.
Collapse
Affiliation(s)
- Xiangyu Guo
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark. .,Danish Pig Research Centre, Danish Agriculture and Food Council, 1609, Copenhagen V, Denmark.
| | - Ahmed Jahoor
- Nordic Seed A/S, 8300, Odder, Denmark.,Department of Plant Breeding, The Swedish University of Agricultural Sciences, 2353, Alnarp, Sweden
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | | |
Collapse
|
11
|
Gao Y, Li J, Cai G, Wang Y, Yang W, Li Y, Zhao X, Li R, Gao Y, Tuo W, Baldwin RL, Li CJ, Fang L, Liu GE. Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide. BMC Genomics 2022; 23:338. [PMID: 35501711 PMCID: PMC9063233 DOI: 10.1186/s12864-022-08562-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 04/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background Gram-negative bacteria are important pathogens in cattle, causing severe infectious diseases, including mastitis. Lipopolysaccharides (LPS) are components of the outer membrane of Gram-negative bacteria and crucial mediators of chronic inflammation in cattle. LPS modulations of bovine immune responses have been studied before. However, the single-cell transcriptomic and chromatin accessibility analyses of bovine peripheral blood mononuclear cells (PBMCs) and their responses to LPS stimulation were never reported. Results We performed single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) in bovine PBMCs before and after LPS treatment and demonstrated that seven major cell types, which included CD4 T cells, CD8 T cells, and B cells, monocytes, natural killer cells, innate lymphoid cells, and dendritic cells. Bioinformatic analyses indicated that LPS could increase PBMC cell cycle progression, cellular differentiation, and chromatin accessibility. Gene analyses further showed significant changes in differential expression, transcription factor binding site, gene ontology, and regulatory interactions during the PBMC responses to LPS. Consistent with the findings of previous studies, LPS induced activation of monocytes and dendritic cells, likely through their upregulated TLR4 receptor. NF-κB was observed to be activated by LPS and an increased transcription of an array of pro-inflammatory cytokines, in agreement that NF-κB is an LPS-responsive regulator of innate immune responses. In addition, by integrating LPS-induced differentially expressed genes (DEGs) with large-scale GWAS of 45 complex traits in Holstein, we detected trait-relevant cell types. We found that selected DEGs were significantly associated with immune-relevant health, milk production, and body conformation traits. Conclusion This study provided the first scRNAseq and scATAC-seq data for cattle PBMCs and their responses to the LPS stimulation to the best of our knowledge. These results should also serve as valuable resources for the future study of the bovine immune system and open the door for discoveries about immune cell roles in complex traits like mastitis at single-cell resolution. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08562-0.
Collapse
Affiliation(s)
- Yahui Gao
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China.,Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA
| | - Jianbin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China.
| | - Gaozhan Cai
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China.,Shandong Ox Livestock Breeding Co., Ltd, Jinan, 250100, China
| | - Yujiao Wang
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Yanqin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Xiuxin Zhao
- Shandong Ox Livestock Breeding Co., Ltd, Jinan, 250100, China
| | - Rongling Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Yundong Gao
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Wenbin Tuo
- Animal Parasitic Diseases Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA
| | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA.
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA.
| |
Collapse
|
12
|
Neumann A, Pingault JB, Felix JF, Jaddoe VWV, Tiemeier H, Cecil C, Walton E. Epigenome-wide contributions to individual differences in childhood phenotypes: a GREML approach. Clin Epigenetics 2022; 14:53. [PMID: 35440009 PMCID: PMC9020033 DOI: 10.1186/s13148-022-01268-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/29/2022] [Indexed: 11/18/2022] Open
Abstract
Background DNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R2Methylation) towards childhood phenotypes is unknown. An estimate of R2Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R2Methylation) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382). Results Using information from > 470,000 autosomal probes we estimated that DNA methylation at birth explains 32% (SDCV = 0.06) of gestational age variance and 5% (SDCV = 0.02) of birth weight variance. The R2Methylation estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations. Conclusions The results suggest that cord blood methylation explains a moderate degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R2Methylation for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.
Collapse
Affiliation(s)
- Alexander Neumann
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands. .,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,VIB Center for Molecular Neurology, Building V of the University of Antwerp (UA) - CDE, Parking 4, Universiteitsplein 1, 2610, Antwerpen, Belgium. .,Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
| | - Jean-Baptiste Pingault
- Division of Psychology and Language Sciences, Department of Clinical, Educational and Health Psychology, University College London, London, UK.,Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Janine F Felix
- The Generation R Study Group, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Charlotte Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.,The Generation R Study Group, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.,Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK
| |
Collapse
|
13
|
Functional annotation of regulatory elements in cattle genome reveals the roles of extracellular interaction and dynamic change of chromatin states in rumen development during weaning. Genomics 2022; 114:110296. [PMID: 35143887 DOI: 10.1016/j.ygeno.2022.110296] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 12/20/2021] [Accepted: 02/01/2022] [Indexed: 12/24/2022]
Abstract
We profiled landscapes of bovine regulatory elements and explored dynamic changes of chromatin states in rumen development during weaning. The regulatory elements (15 chromatin states) and their coordinated activities in cattle were defined through genome-wide profiling of four histone modifications, CTCF-binding, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial tissues. Each chromatin state presented specific enrichment for sequence ontology, methylation, trait-associated variants, transcription, gene expression-associated variants, selection signatures, and evolutionarily conserved elements. During weaning, weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states and occurred in tandem with significant variations in gene expression and DNA methylation, significantly associated with stature, production, and reproduction economic traits. By comparing with in vitro cultured epithelial cells and in vivo rumen tissues, we showed the commonness and uniqueness of these results, especially the roles of cell interactions and mitochondrial activities in tissue development.
Collapse
|
14
|
Kloeve-Mogensen K, Rohde PD, Twisttmann S, Nygaard M, Koldby KM, Steffensen R, Dahl CM, Rytter D, Overgaard MT, Forman A, Christiansen L, Nyegaard M. Polygenic Risk Score Prediction for Endometriosis. FRONTIERS IN REPRODUCTIVE HEALTH 2021; 3:793226. [PMID: 36303976 PMCID: PMC9580817 DOI: 10.3389/frph.2021.793226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/09/2021] [Indexed: 12/19/2022] Open
Abstract
Endometriosis is a major health care challenge because many young women with endometriosis go undetected for an extended period, which may lead to pain sensitization. Clinical tools to better identify candidates for laparoscopy-guided diagnosis are urgently needed. Since endometriosis has a strong genetic component, there is a growing interest in using genetics as part of the clinical risk assessment. The aim of this work was to investigate the discriminative ability of a polygenic risk score (PRS) for endometriosis using three different cohorts: surgically confirmed cases from the Western Danish endometriosis referral Center (249 cases, 348 controls), cases identified from the Danish Twin Registry (DTR) based on ICD-10 codes from the National Patient Registry (140 cases, 316 controls), and replication analysis in the UK Biobank (2,967 cases, 256,222 controls). Patients with adenomyosis from the DTR (25 cases) and from the UK Biobank (1,883 cases) were included for comparison. The PRS was derived from 14 genetic variants identified in a published genome-wide association study with more than 17,000 cases. The PRS was associated with endometriosis in surgically confirmed cases [odds ratio (OR) = 1.59, p = 2.57× 10−7] and in cases from the DTR biobank (OR = 1.50, p = 0.0001). Combining the two Danish cohorts, each standard deviation increase in PRS was associated with endometriosis (OR = 1.57, p = 2.5× 10−11), as well as the major subtypes of endometriosis; ovarian (OR = 1.72, p = 6.7× 10−5), infiltrating (OR = 1.66, p = 2.7× 10−9), and peritoneal (OR = 1.51, p = 2.6 × 10−3). These findings were replicated in the UK Biobank with a much larger sample size (OR = 1.28, p < 2.2× 10−16). The PRS was not associated with adenomyosis, suggesting that adenomyosis is not driven by the same genetic risk variants as endometriosis. Our results suggest that a PRS captures an increased risk of all types of endometriosis rather than an increased risk for endometriosis in specific locations. Although the discriminative accuracy is not yet sufficient as a stand-alone clinical utility, our data demonstrate that genetics risk variants in form of a simple PRS may add significant new discriminatory value. We suggest that an endometriosis PRS in combination with classical clinical risk factors and symptoms could be an important step in developing an urgently needed endometriosis risk stratification tool.
Collapse
Affiliation(s)
- Kirstine Kloeve-Mogensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Palle Duun Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Simone Twisttmann
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | | | - Rudi Steffensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Christian Møller Dahl
- Department of Business and Economics, University of Southern Denmark, Odense, Denmark
| | - Dorte Rytter
- Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
| | | | - Axel Forman
- Department of Gynecology and Obstetrics, Aarhus University Hospital, Skejby, Denmark
| | - Lene Christiansen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- *Correspondence: Mette Nyegaard
| |
Collapse
|
15
|
Washburn JD, Cimen E, Ramstein G, Reeves T, O'Briant P, McLean G, Cooper M, Hammer G, Buckler ES. Predicting phenotypes from genetic, environment, management, and historical data using CNNs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3997-4011. [PMID: 34448888 DOI: 10.1007/s00122-021-03943-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Deep neural networks such as Multilayer Perceptrons (MPL) and Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on held-out G, E, and M data (r = 0.50 vs. r = 0.43), and performed slightly worse than standard methods when only G was held out (r = 0.74 vs. r = 0.80). Pre-training on historical data increased accuracy compared to trial data alone. Saliency map analysis indicated the CNN has "learned" to prioritize many factors of known agricultural importance.
Collapse
Affiliation(s)
- Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service, Columbia, MO, 65211, USA.
| | - Emre Cimen
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Computational Intelligence and Optimization Laboratory, Industrial Engineering Department, Eskisehir Technical University, Eskisehir, Turkey
| | - Guillaume Ramstein
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - Timothy Reeves
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Patrick O'Briant
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Greg McLean
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Department of Agriculture, Agricultural Research Service, Ithaca, NY, 14850, USA
| |
Collapse
|
16
|
Rohde PD, Nyegaard M, Kjolby M, Sørensen P. Multi-Trait Genomic Risk Stratification for Type 2 Diabetes. Front Med (Lausanne) 2021; 8:711208. [PMID: 34568370 PMCID: PMC8455930 DOI: 10.3389/fmed.2021.711208] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 08/05/2021] [Indexed: 01/14/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40-70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50-94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.
Collapse
Affiliation(s)
- Palle Duun Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.,Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.,Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Mads Kjolby
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.,Department of Population Health and Genomics, University of Dundee, Dundee, United Kingdom.,Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark.,Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Peter Sørensen
- Centre for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| |
Collapse
|
17
|
Zhao B, Luo H, He J, Huang X, Chen S, Fu X, Zeng W, Tian Y, Liu S, Li CJ, Liu GE, Fang L, Zhang S, Tian K. Comprehensive transcriptome and methylome analysis delineates the biological basis of hair follicle development and wool-related traits in Merino sheep. BMC Biol 2021; 19:197. [PMID: 34503498 PMCID: PMC8427949 DOI: 10.1186/s12915-021-01127-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/18/2021] [Indexed: 12/13/2022] Open
Abstract
Background Characterization of the molecular mechanisms underlying hair follicle development is of paramount importance in the genetic improvement of wool-related traits in sheep and skin-related traits in humans. The Merino is the most important breed of fine-wooled sheep in the world. In this study, we systematically investigated the complexity of sheep hair follicle development by integrating transcriptome and methylome datasets from Merino sheep skin. Results We analysed 72 sequence datasets, including DNA methylome and the whole transcriptome of four gene types, i.e. protein-coding genes (PCGs), lncRNAs, circRNAs, and miRNAs, across four embryonic days (E65, E85, E105, and E135) and two postnatal days (P7 and P30) from the skin tissue of 18 Merino sheep. We revealed distinct expression profiles of these four gene types across six hair follicle developmental stages, and demonstrated their complex interactions with DNA methylation. PCGs with stage-specific expression or regulated by stage-specific lncRNAs, circRNAs, and miRNAs were significantly enriched in epithelial differentiation and hair follicle morphogenesis. Regulatory network and gene co-expression analyses identified key transcripts controlling hair follicle development. We further predicted transcriptional factors (e.g. KLF4, LEF1, HOXC13, RBPJ, VDR, RARA, and STAT3) with stage-specific involvement in hair follicle morphogenesis. Through integrating these stage-specific genomic features with results from genome-wide association studies (GWAS) of five wool-related traits in 7135 Merino sheep, we detected developmental stages and genes that were relevant with wool-related traits in sheep. For instance, genes that were specifically upregulated at E105 were significantly associated with most of wool-related traits. A phenome-wide association study (PheWAS) demonstrated that candidate genes of wool-related traits (e.g. SPHK1, GHR, PPP1R27, CSRP2, EEF1A2, and PTPN1) in sheep were also significantly associated with dermatological, metabolic, and immune traits in humans. Conclusions Our study provides novel insights into the molecular basis of hair follicle morphogenesis and will serve as a foundation to improve breeding for wool traits in sheep. It also indicates the importance of studying gene expression in the normal development of organs in understanding the genetic architecture of economically important traits in livestock. The datasets generated here are useful resources for functionally annotating the sheep genome, and for elucidating early skin development in mammals, including humans. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-021-01127-9.
Collapse
Affiliation(s)
- Bingru Zhao
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Hanpeng Luo
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Junmin He
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Xixia Huang
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Siqian Chen
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xuefeng Fu
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Weidan Zeng
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Yuezhen Tian
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Shuli Liu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, Maryland, USA
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, Maryland, USA
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Shengli Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
| | - Kechuan Tian
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China.
| |
Collapse
|
18
|
Zhao B, Luo H, Huang X, Wei C, Di J, Tian Y, Fu X, Li B, Liu GE, Fang L, Zhang S, Tian K. Integration of a single-step genome-wide association study with a multi-tissue transcriptome analysis provides novel insights into the genetic basis of wool and weight traits in sheep. Genet Sel Evol 2021; 53:56. [PMID: 34193030 PMCID: PMC8247193 DOI: 10.1186/s12711-021-00649-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 06/22/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW). RESULTS Our dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data (n = 372,534). The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from - 0.44 (CN vs. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples (representing 87 tissue types from 16 animals), we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans. CONCLUSIONS Our findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.
Collapse
Affiliation(s)
- Bingru Zhao
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Hanpeng Luo
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xixia Huang
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Chen Wei
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Jiang Di
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Yuezhen Tian
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Xuefeng Fu
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, EH25 9RG, UK
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, MD, USA
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Shengli Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
| | - Kechuan Tian
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China.
| |
Collapse
|
19
|
Rohde PD, Kristensen TN, Sarup P, Muñoz J, Malmendal A. Prediction of complex phenotypes using the Drosophila melanogaster metabolome. Heredity (Edinb) 2021; 126:717-732. [PMID: 33510469 PMCID: PMC8102504 DOI: 10.1038/s41437-021-00404-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 01/30/2023] Open
Abstract
Understanding the genotype-phenotype map and how variation at different levels of biological organization is 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 and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show 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, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. 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 is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.
Collapse
Affiliation(s)
- Palle Duun Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
| | - Torsten Nygaard Kristensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Animal Science, Aarhus University, Tjele, Denmark
| | - Pernille Sarup
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- Nordic Seed A/S, Odder, Denmark
| | - Joaquin Muñoz
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Anders Malmendal
- Department of Science and Environment, Roskilde University, Roskilde, Denmark.
| |
Collapse
|
20
|
Gao Y, Fang L, Baldwin RL, Connor EE, Cole JB, Van Tassell CP, Ma L, Li CJ, Liu GE. Single-cell transcriptomic analyses of dairy cattle ruminal epithelial cells during weaning. Genomics 2021; 113:2045-2055. [PMID: 33933592 DOI: 10.1016/j.ygeno.2021.04.039] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 03/20/2021] [Accepted: 04/27/2021] [Indexed: 11/30/2022]
Abstract
Using the 10× Genomics Chromium Controller, we obtained scRNA-seq data of 5064 and 1372 individual cells from two Holstein calf ruminal epithelial tissues before and after weaning, respectively. We detected six distinct cell clusters, designated their cell types, and reported their marker genes. We then examined these clusters' underlining cell types and relationships by performing cell cycle, pseudotime trajectory, regulatory network, weighted gene co-expression network and gene ontology analyses. By integrating these cell marker genes with Holstein GWAS signals, we found they were enriched for animal production and body conformation traits. Finally, we confirmed their cell identities by comparing them with human and mouse stomach epithelial cells. This study presents an initial effort to implement single-cell transcriptomic analysis in cattle, and demonstrates ruminal tissue epithelial cell types and their developments during weaning, opening the door for new discoveries about tissue/cell type roles in complex traits at single-cell resolution.
Collapse
Affiliation(s)
- Yahui Gao
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA; Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA.
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom.
| | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - Erin E Connor
- Department of Animal and Food Sciences, University of Delaware, Newark, DE 19716, USA.
| | - John B Cole
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA.
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| |
Collapse
|
21
|
Møller PL, Rohde PD, Winther S, Breining P, Nissen L, Nykjaer A, Bøttcher M, Nyegaard M, Kjolby M. Sortilin as a Biomarker for Cardiovascular Disease Revisited. Front Cardiovasc Med 2021; 8:652584. [PMID: 33937362 PMCID: PMC8085299 DOI: 10.3389/fcvm.2021.652584] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
Genetic variants in the genomic region containing SORT1 (encoding the protein sortilin) are strongly associated with cholesterol levels and the risk of coronary artery disease (CAD). Circulating sortilin has therefore been proposed as a potential biomarker for cardiovascular disease. Multiple studies have reported association between plasma sortilin levels and cardiovascular outcomes. However, the findings are not consistent across studies, and most studies have small sample sizes. The aim of this study was to evaluate sortilin as a biomarker for CAD in a well-characterized cohort with symptoms suggestive of CAD. In total, we enrolled 1,173 patients with suspected stable CAD referred to coronary computed tomography angiography. Sortilin was measured in plasma using two different technologies for quantifying circulating sortilin: a custom-made enzyme-linked immunosorbent assay (ELISA) and OLINK Cardiovascular Panel II. We found a relative poor correlation between the two methods (correlation coefficient = 0.21). In addition, genotyping and whole-genome sequencing were performed on all patients. By whole-genome regression analysis of sortilin levels measured with ELISA and OLINK, two independent cis protein quantitative trait loci (pQTL) on chromosome 1p13.3 were identified, with one of them being a well-established risk locus for CAD. Incorporating rare genetic variants from whole-genome sequence data did not identify any additional pQTLs for plasma sortilin. None of the traditional CAD risk factors, such as sex, age, smoking, and statin use, were associated with plasma sortilin levels. Furthermore, there was no association between circulating sortilin levels and coronary artery calcium score (CACS) or disease severity. Sortilin did not improve discrimination of obstructive CAD, when added to a clinical pretest probability (PTP) model for CAD. Overall, our results indicate that studies using different methodologies for measuring circulating sortilin should be compared with caution. In conclusion, the well-known SORT1 risk locus for CAD is linked to lower sortilin levels in circulation, measured with ELISA; however, the effect sizes are too small for sortilin to be a useful biomarker for CAD in a clinical setting of low- to intermediate-risk chest-pain patients.
Collapse
Affiliation(s)
| | - Palle D. Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Simon Winther
- Department of Cardiology, Gødstrup Hospital, NIDO, Herning, Denmark
| | - Peter Breining
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- PROMEMO and DANDRITE, Aarhus University, Aarhus, Denmark
| | - Louise Nissen
- Department of Cardiology, Gødstrup Hospital, NIDO, Herning, Denmark
| | - Anders Nykjaer
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- PROMEMO and DANDRITE, Aarhus University, Aarhus, Denmark
| | - Morten Bøttcher
- Department of Cardiology, Gødstrup Hospital, NIDO, Herning, Denmark
| | - Mette Nyegaard
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Mads Kjolby
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- PROMEMO and DANDRITE, Aarhus University, Aarhus, Denmark
- Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| |
Collapse
|
22
|
Nordestgaard NV, Thach T, Sarup P, Rodriguez-Algaba J, Andersen JR, Hovmøller MS, Jahoor A, Jørgensen LN, Orabi J. Multi-Parental Populations Suitable for Identifying Sources of Resistance to Powdery Mildew in Winter Wheat. FRONTIERS IN PLANT SCIENCE 2021; 11:570863. [PMID: 33552092 PMCID: PMC7859110 DOI: 10.3389/fpls.2020.570863] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 12/02/2020] [Indexed: 06/12/2023]
Abstract
Wheat (Triticum aestivum L.) is one of the world's staple food crops and one of the most devastating foliar diseases attacking wheat is powdery mildew (PM). In Denmark only a few specific fungicides are available for controlling PM and the use of resistant cultivars is often recommended. In this study, two Chinese wheat landraces and two synthetic hexaploid wheat lines were used as donors for creating four multi-parental populations with a total of 717 individual lines to identify new PM resistance genetic variants. These lines and the nine parental lines (including the elite cultivars used to create the populations) were genotyped using a 20 K Illumina SNP chip, which resulted in 8,902 segregating single nucleotide polymorphisms for assessment of the population structure and whole genome association study. The largest genetic difference among the lines was between the donors and the elite cultivars, the second largest genetic difference was between the different donors; a difference that was also reflected in differences between the four multi-parental populations. The 726 lines were phenotyped for PM resistance in 2017 and 2018. A high PM disease pressure was observed in both seasons, with severities ranging from 0 to >50%. Whole genome association studies for genetic variation in PM resistance in the populations revealed significant markers mapped to either chromosome 2A, B, or D in each of the four populations. However, linkage disequilibrium between these putative quantitative trait loci (QTL) were all above 0.80, probably representing a single QTL. A combined analysis of all the populations confirmed this result and the most associated marker explained 42% of the variation in PM resistance. This study gives both knowledge about the resistance as well as molecular tools and plant material that can be utilised in marker-assisted selection. Additionally, the four populations produced in this study are highly suitable for association studies of other traits than PM resistance.
Collapse
Affiliation(s)
| | - Tine Thach
- Department of Agroecology, Aarhus University, Slagelse, Denmark
| | | | | | | | | | - Ahmed Jahoor
- Nordic Seed A/S, Odder, Denmark
- Department of Plant Breeding, The Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | | |
Collapse
|
23
|
Farooq M, van Dijk ADJ, Nijveen H, Aarts MGM, Kruijer W, Nguyen TP, Mansoor S, de Ridder D. Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana. Front Genet 2021; 11:609117. [PMID: 33552126 PMCID: PMC7855462 DOI: 10.3389/fgene.2020.609117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/21/2020] [Indexed: 01/11/2023] Open
Abstract
Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.
Collapse
Affiliation(s)
- Muhammad Farooq
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
- Molecular Virology and Gene Silencing Lab, Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Punjab, Pakistan
| | - Aalt D. J. van Dijk
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
- Biometris, Wageningen University, Wageningen, Netherlands
| | - Harm Nijveen
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| | - Mark G. M. Aarts
- Laboratory of Genetics, Wageningen University, Wageningen, Netherlands
| | - Willem Kruijer
- Biometris, Wageningen University, Wageningen, Netherlands
| | - Thu-Phuong Nguyen
- Laboratory of Genetics, Wageningen University, Wageningen, Netherlands
| | - Shahid Mansoor
- Molecular Virology and Gene Silencing Lab, Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Punjab, Pakistan
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| |
Collapse
|
24
|
Hansen KB, Staehr C, Rohde PD, Homilius C, Kim S, Nyegaard M, Matchkov VV, Boedtkjer E. PTPRG is an ischemia risk locus essential for HCO 3--dependent regulation of endothelial function and tissue perfusion. eLife 2020; 9:e57553. [PMID: 32955439 PMCID: PMC7541084 DOI: 10.7554/elife.57553] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 09/18/2020] [Indexed: 12/22/2022] Open
Abstract
Acid-base conditions modify artery tone and tissue perfusion but the involved vascular-sensing mechanisms and disease consequences remain unclear. We experimentally investigated transgenic mice and performed genetic studies in a UK-based human cohort. We show that endothelial cells express the putative HCO3--sensor receptor-type tyrosine-protein phosphatase RPTPγ, which enhances endothelial intracellular Ca2+-responses in resistance arteries and facilitates endothelium-dependent vasorelaxation only when CO2/HCO3- is present. Consistent with waning RPTPγ-dependent vasorelaxation at low [HCO3-], RPTPγ limits increases in cerebral perfusion during neuronal activity and augments decreases in cerebral perfusion during hyperventilation. RPTPγ does not influence resting blood pressure but amplifies hyperventilation-induced blood pressure elevations. Loss-of-function variants in PTPRG, encoding RPTPγ, are associated with increased risk of cerebral infarction, heart attack, and reduced cardiac ejection fraction. We conclude that PTPRG is an ischemia susceptibility locus; and RPTPγ-dependent sensing of HCO3- adjusts endothelium-mediated vasorelaxation, microvascular perfusion, and blood pressure during acid-base disturbances and altered tissue metabolism.
Collapse
Affiliation(s)
| | | | - Palle D Rohde
- Department of Chemistry and Bioscience, Aalborg UniversityAalborgDenmark
| | | | - Sukhan Kim
- Department of Biomedicine, Aarhus UniversityAarhusDenmark
| | - Mette Nyegaard
- Department of Biomedicine, Aarhus UniversityAarhusDenmark
| | | | - Ebbe Boedtkjer
- Department of Biomedicine, Aarhus UniversityAarhusDenmark
| |
Collapse
|
25
|
Yan Z, Huang H, Freebern E, Santos DJA, Dai D, Si J, Ma C, Cao J, Guo G, Liu GE, Ma L, Fang L, Zhang Y. Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle. BMC Genomics 2020; 21:489. [PMID: 32680461 PMCID: PMC7367229 DOI: 10.1186/s12864-020-06909-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/13/2020] [Indexed: 01/12/2023] Open
Abstract
Background Ketosis is a common metabolic disease during the transition period in dairy cattle, resulting in long-term economic loss to the dairy industry worldwide. While genetic selection of resistance to ketosis has been adopted by many countries, the genetic and biological basis underlying ketosis is poorly understood. Results We collected a total of 24 blood samples from 12 Holstein cows, including 4 healthy and 8 ketosis-diagnosed ones, before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) from leukocytes and plasma in each of these samples, respectively. By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein). By conducting genome-wide association signal (GWAS) enrichment analysis among six common health traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found that 4 out of 16 modules were genetically (FDR < 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA. We further identified five candidate genes for ketosis, including GRINA, MAF1, MAFA, C14H8orf82 and RECQL4. Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans. For instance, MAFA, which is involved in insulin secretion, glucose response, and transcriptional regulation, showed a significantly higher association with metabolic and endocrine traits compared to other types of traits in humans. Conclusions In summary, our study provides novel insights into the molecular mechanism underlying ketosis in cattle, and highlights that an integrative analysis of omics data and cross-species mapping are promising for illustrating the genetic architecture underpinning complex traits.
Collapse
Affiliation(s)
- Ze Yan
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Hetian Huang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.,College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, 450002, China
| | - Ellen Freebern
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Daniel J A Santos
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Dongmei Dai
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jingfang Si
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Chong Ma
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Jie Cao
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Gang Guo
- Beijing Sunlon Livestock Development Co Ltd., Beijing, 100076, China
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - Yi Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
| |
Collapse
|
26
|
Liu S, Yu Y, Zhang S, Cole JB, Tenesa A, Wang T, McDaneld TG, Ma L, Liu GE, Fang L. Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human. BMC Biol 2020; 18:80. [PMID: 32620158 PMCID: PMC7334855 DOI: 10.1186/s12915-020-00792-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 05/12/2020] [Indexed: 02/01/2023] Open
Abstract
Background Lack of comprehensive functional annotations across a wide range of tissues and cell types severely hinders the biological interpretations of phenotypic variation, adaptive evolution, and domestication in livestock. Here we used a combination of comparative epigenomics, genome-wide association study (GWAS), and selection signature analysis, to shed light on potential adaptive evolution in cattle. Results We cross-mapped 8 histone marks of 1300 samples from human to cattle, covering 178 unique tissues/cell types. By uniformly analyzing 723 RNA-seq and 40 whole genome bisulfite sequencing (WGBS) datasets in cattle, we validated that cross-mapped histone marks captured tissue-specific expression and methylation, reflecting tissue-relevant biology. Through integrating cross-mapped tissue-specific histone marks with large-scale GWAS and selection signature results, we for the first time detected relevant tissues and cell types for 45 economically important traits and artificial selection in cattle. For instance, immune tissues are significantly associated with health and reproduction traits, multiple tissues for milk production and body conformation traits (reflecting their highly polygenic architecture), and thyroid for the different selection between beef and dairy cattle. Similarly, we detected relevant tissues for 58 complex traits and diseases in humans and observed that immune and fertility traits in humans significantly correlated with those in cattle in terms of relevant tissues, which facilitated the identification of causal genes for such traits. For instance, PIK3CG, a gene highly specifically expressed in mononuclear cells, was significantly associated with both age-at-menopause in human and daughter-still-birth in cattle. ICAM, a T cell-specific gene, was significantly associated with both allergic diseases in human and metritis in cattle. Conclusion Collectively, our results highlighted that comparative epigenomics in conjunction with GWAS and selection signature analyses could provide biological insights into the phenotypic variation and adaptive evolution. Cattle may serve as a model for human complex traits, by providing additional information beyond laboratory model organisms, particularly when more novel phenotypes become available in the near future.
Collapse
Affiliation(s)
- Shuli Liu
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA.,College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA
| | - Albert Tenesa
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.,The Roslin Institute, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Ting Wang
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Tara G McDaneld
- US Meat Animal Research Center, Agricultural Research Service, USDA, Clay Center, NE, 68933, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA.
| | - Lingzhao Fang
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA. .,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. .,Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| |
Collapse
|
27
|
Fang L, Cai W, Liu S, Canela-Xandri O, Gao Y, Jiang J, Rawlik K, Li B, Schroeder SG, Rosen BD, Li CJ, Sonstegard TS, Alexander LJ, Van Tassell CP, VanRaden PM, Cole JB, Yu Y, Zhang S, Tenesa A, Ma L, Liu GE. Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle. Genome Res 2020; 30:790-801. [PMID: 32424068 PMCID: PMC7263193 DOI: 10.1101/gr.250704.119] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 05/01/2020] [Indexed: 12/13/2022]
Abstract
By uniformly analyzing 723 RNA-seq data from 91 tissues and cell types, we built a comprehensive gene atlas and studied tissue specificity of genes in cattle. We demonstrated that tissue-specific genes significantly reflected the tissue-relevant biology, showing distinct promoter methylation and evolution patterns (e.g., brain-specific genes evolve slowest, whereas testis-specific genes evolve fastest). Through integrative analyses of those tissue-specific genes with large-scale genome-wide association studies, we detected relevant tissues/cell types and candidate genes for 45 economically important traits in cattle, including blood/immune system (e.g., CCDC88C) for male fertility, brain (e.g., TRIM46 and RAB6A) for milk production, and multiple growth-related tissues (e.g., FGF6 and CCND2) for body conformation. We validated these findings by using epigenomic data across major somatic tissues and sperm. Collectively, our findings provided novel insights into the genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas can serve as a primary source for biological interpretation, functional validation, studies of adaptive evolution, and genomic improvement in livestock.
Collapse
Affiliation(s)
- Lingzhao Fang
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
- Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Wentao Cai
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Oriol Canela-Xandri
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
- Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Yahui Gao
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - Jicai Jiang
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
| | - Bingjie Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Steven G Schroeder
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | | | - Leeson J Alexander
- Fort Keogh Livestock and Range Research Laboratory, Agricultural Research Service, USDA, Miles City, Montana 59301, USA
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Paul M VanRaden
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - John B Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
- Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| |
Collapse
|