1
|
Zhong Z, Li G, Xu Z, Zeng H, Teng J, Feng X, Diao S, Gao Y, Li J, Zhang Z. Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations. Anim Genet 2024; 55:265-276. [PMID: 38185881 DOI: 10.1111/age.13394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/24/2023] [Accepted: 12/21/2023] [Indexed: 01/09/2024]
Abstract
In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p-values (pC ). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (pC < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.
Collapse
Affiliation(s)
- Zhanming Zhong
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Guangzhen Li
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zhiting Xu
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Haonan Zeng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jinyan Teng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xueyan Feng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Shuqi Diao
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Yahui Gao
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jiaqi Li
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zhe Zhang
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| |
Collapse
|
2
|
Cui L, Yang B, Xiao S, Gao J, Baud A, Graham D, McBride M, Dominiczak A, Schafer S, Aumatell RL, Mont C, Teruel AF, Hübner N, Flint J, Mott R, Huang L. Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing. Genome Biol 2023; 24:215. [PMID: 37773188 PMCID: PMC10540365 DOI: 10.1186/s13059-023-03060-2] [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: 03/31/2023] [Accepted: 09/18/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively. RESULTS We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance. CONCLUSIONS Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.
Collapse
Affiliation(s)
- Leilei Cui
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
- UCL Genetics Institute, University College London, London, WC1E 6BT, UK
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- School of Life Sciences, Nanchang University, Nanchang, China
| | - Bin Yang
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
| | - Shijun Xiao
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
| | - Jun Gao
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
| | - Amelie Baud
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Delyth Graham
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Martin McBride
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Anna Dominiczak
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Sebastian Schafer
- Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Regina Lopez Aumatell
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Carme Mont
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Albert Fernandez Teruel
- Departamento de Psiquiatría y Medicina Legal, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Norbert Hübner
- Genetics and Genomics of Cardiovascular Diseases Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Center for Cardiovascular Research) Partner Site Berlin, Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jonathan Flint
- Department of Psychiatry and Behavioral Sciences, Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Richard Mott
- UCL Genetics Institute, University College London, London, WC1E 6BT, UK.
| | - Lusheng Huang
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China.
| |
Collapse
|
3
|
Wei C, Zeng H, Zhong Z, Cai X, Teng J, Liu Y, Zhao Y, Wu X, Li J, Zhang Z. Integration of non-additive genome-wide association study with a multi-tissue transcriptome analysis of growth and carcass traits in Duroc pigs. Animal 2023; 17:100817. [PMID: 37196577 DOI: 10.1016/j.animal.2023.100817] [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: 12/08/2022] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 05/19/2023] Open
Abstract
Growth and carcass traits are of economic importance in the pig production, which affect pork quality and profitability of finishing pig production. This study used whole-genome and transcriptome sequencing technologies to identify potential candidate genes affecting growth and carcass traits in Duroc pigs. The medium (50-60 k) single nucleotide polymorphism (SNP) arrays of 4 154 Duroc pigs from three populations were imputed to whole-genome sequence data, yielding 10 463 227 markers on 18 autosomes. The dominance heritabilities estimated for growth and carcass traits ranged from 0.000 ± 0.041 to 0.161 ± 0.054. Using non-additive genome-wide association study (GWAS), we identified 80 dominance quantitative trait loci for growth and carcass traits at genome-wide significance (false discovery rate < 5%), 15 of which were also detected in our additive GWAS. After fine mapping, 31 candidate genes for dominance GWAS were annotated, and 8 of them were highlighted that have been previously reported to be associated with growth and development (e.g. SNX14, RELN and ENPP2), autosomal recessive diseases (e.g. AMPH, SNX14, RELN and CACNB4) and immune response (e.g. UNC93B1 and PPM1D). By integrating the lead SNPs with RNA-seq data of 34 pig tissues from the Pig Genotype-Tissue Expression project (https://piggtex.farmgtex.org/), we found that the rs691128548, rs333063869, and rs1110730611 have significantly dominant effects for the expression of SNX14, AMPH and UNC93B1 genes in tissues related to growth and development for pig, respectively. Finally, the identified candidate genes were significantly enriched for biological processes involved in the cell and organ development, lipids catabolic process and phosphatidylinositol 3-kinase signalling (P < 0.05). These results provide new molecular markers for meat production and quality selection of pig as well as basis for deciphering the genetic mechanisms of growth and carcass traits.
Collapse
Affiliation(s)
- Chen Wei
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China
| | - Haonan Zeng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China
| | - Zhanming Zhong
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China
| | - Xiaodian Cai
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China
| | - Jingyan Teng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China
| | - Yuqiang Liu
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China
| | - Yunxiang Zhao
- School of Life Science and Engineering, Foshan University, Foshan 528225, PR China
| | - Xibo Wu
- Guangxi Guiken Yongxin Animal Husbandry Group Co. Ltd, Nanning 530000, PR China
| | - Jiaqi Li
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China
| | - Zhe Zhang
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China.
| |
Collapse
|
4
|
Heidaritabar M, Bink MCAM, Dervishi E, Charagu P, Huisman A, Plastow GS. Genome-wide association studies for additive and dominance effects for body composition traits in commercial crossbred Piétrain pigs. J Anim Breed Genet 2023. [PMID: 36883263 DOI: 10.1111/jbg.12768] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/18/2023] [Indexed: 03/09/2023]
Abstract
Fat depth (FD) and muscle depth (MD) are economically important traits and used to estimate carcass lean content (LMP), which is one of the main breeding objectives in pig breeding programmes. We assessed the genetic architectures of body composition traits for additive and dominance effects in commercial crossbred Piétrain pigs using both 50 K array and sequence genotypes. We first performed a genome-wide association study (GWAS) using single-marker association analysis with a false discovery rate of 0.1. Then, we estimated the additive and dominance effects of the most significant variant in the quantitative trait loci (QTL) regions. It was investigated whether the use of whole-genome sequence (WGS) will improve the QTL detection (both additive and dominance) with a higher power compared with lower density SNP arrays. Our results showed that more QTL regions were detected by WGS compared with 50 K array (n = 54 vs. n = 17). Of the novel associated regions associated with FD and LMP and detected by WGS, the most pronounced peak was on SSC13, situated at ~116-118, 121-127 and 129-134 Mbp. Additionally, we found that only additive effects contributed to the genetic architecture of the analysed traits and no significant dominance effects were found for the tested SNPs at QTL regions, regardless of panel density. The associated SNPs are located in or near several relevant candidate genes. Of these genes, GABRR2, GALR1, RNGTT, CDH20 and MC4R have been previously reported as being associated with fat deposition traits. However, the genes on SSC1 (ZNF292, ORC3, CNR1, SRSF12, MDN1, TSHZ1, RELCH and RNF152) and SSC18 (TTC26 and KIAA1549) have not been reported previously to our best knowledge. Our current findings provide insights into the genomic regions influencing composition traits in Piétrain pigs.
Collapse
Affiliation(s)
- Marzieh Heidaritabar
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Marco C A M Bink
- Hendrix Genetics Research, Technology & Services B.V., Boxmeer, the Netherlands
| | - Elda Dervishi
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Patrick Charagu
- Hendrix Genetics, Swine Business Unit, Regina, Saskatchewan, Canada
| | - Abe Huisman
- Hendrix Genetics Research, Technology & Services B.V., Boxmeer, the Netherlands
| | - Graham S Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| |
Collapse
|
5
|
Zeng H, Zhong Z, Xu Z, Teng J, Wei C, Chen Z, Zhang W, Ding X, Li J, Zhang Z. Meta-analysis of genome-wide association studies uncovers shared candidate genes across breeds for pig fatness trait. BMC Genomics 2022; 23:786. [DOI: 10.1186/s12864-022-09036-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 11/22/2022] [Indexed: 12/02/2022] Open
Abstract
Abstract
Background
Average backfat thickness (BFT) is a critical complex trait in pig and an important indicator for fat deposition and lean rate. Usually, genome-wide association study (GWAS) was used to discover quantitative trait loci (QTLs) of BFT in a single population. However, the power of GWAS is limited by sample size in a single population. Alternatively, meta-analysis of GWAS (metaGWAS) is an attractive method to increase the statistical power by integrating data from multiple breeds and populations. The aim of this study is to identify shared genetic characterization of BFT across breeds in pigs via metaGWAS.
Results
In this study, we performed metaGWAS on BFT using 15,353 pigs (5,143 Duroc, 7,275 Yorkshire, and 2,935 Landrace) from 19 populations. We detected 40 genome-wide significant SNPs (Bonferroni corrected P < 0.05) and defined five breed-shared QTLs in across-breed metaGWAS. Markers within the five QTL regions explained 7 ~ 9% additive genetic variance and showed strong heritability enrichment. Furthermore, by integrating information from multiple bioinformatics databases, we annotated 46 candidate genes located in the five QTLs. Among them, three important (MC4R, PPARD, and SLC27A1) and seven suggestive candidate genes (PHLPP1, NUDT3, ILRUN, RELCH, KCNQ5, ITPR3, and U3) were identified.
Conclusion
QTLs and candidate genes underlying BFT across breeds were identified via metaGWAS from multiple populations. Our findings contribute to the understanding of the genetic architecture of BFT and the regulating mechanism underlying fat deposition in pigs.
Collapse
|
6
|
Genome-Wide Association Study of Growth Traits in a Four-Way Crossbred Pig Population. Genes (Basel) 2022; 13:genes13111990. [DOI: 10.3390/genes13111990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/04/2022] Open
Abstract
Growth traits are crucial economic traits in the commercial pig industry and have a substantial impact on pig production. However, the genetic mechanism of growth traits is not very clear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to analyze ten growth traits on 223 four-way intercross pigs. A total of 227,921 highly consistent single nucleotide polymorphisms (SNPs) uniformly dispersed throughout the entire genome were used to conduct GWAS. A total of 53 SNPs were identified for ten growth traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. Two novel QTLs on SSC4 and SSC7 were related to average daily gain from 30 to 60 kg (ADG30–60) and body length (BL), respectively. Furthermore, 13 candidate genes (ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were found to be associated with growth traits in pigs. The GWAS findings will enhance our comprehension of the genetic architecture of growth traits. We suggested that these detected SNPs and corresponding candidate genes might provide a biological foundation for improving the growth and production performance of pigs in swine breeding.
Collapse
|