1
|
Shi L, Li Y, Liu Q, Zhang L, Wang L, Liu X, Gao H, Hou X, Zhao F, Yan H, Wang L. Identification of SNPs and Candidate Genes for Milk Production Ability in Yorkshire Pigs. Front Genet 2021; 12:724533. [PMID: 34675963 PMCID: PMC8523896 DOI: 10.3389/fgene.2021.724533] [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: 06/13/2021] [Accepted: 09/22/2021] [Indexed: 12/01/2022] Open
Abstract
Sow milk production ability is an important limiting factor impacting suboptimal growth and the survival of piglets. Through pig genetic improvement, litter sizes have been increased. Larger litters need more suckling mammary glands, which results in increased milk from the lactating sow. Hence, there is much significance to exploring sow lactation performance. For milk production ability, it is not practical to directly measure the milk yield, we used litter weight gain (LWG) throughout sow lactation as an indicator. In this study, we estimated the heritability of LWG, namely, 0.18 ± 0.07. We then performed a GWAS, and detected seven significant SNPs, namely, Sus scrofa Chromosome (SSC) 2: ASGA0010040 (p = 7.73E-11); SSC2:MARC0029355 (p = 1.30E-08), SSC6: WU_10.2_6_65751151 (p = 1.32E-10), SSC7: MARC0058875 (p = 4.99E-09), SSC10: WU_10.2_10_49571394 (p = 6.79E-08), SSC11: M1GA0014659 (p = 1.19E-07), and SSC15: MARC0042106 (p = 1.16E-07). We performed the distribution of phenotypes corresponding to the genotypes of seven significant SNPs and showed that ASGA0010040, MARC0029355, MARC0058875, WU_10.2_10_49571394, M1GA0014659, and MARC0042106 had extreme phenotypic values that corresponded to the homozygous genotypes, while the intermediate values corresponded to the heterozygous genotypes. We screened for flanking regions ± 200 kb nearby the seven significant SNPs, and identified 38 genes in total. Among them, 28 of the candidates were involved in lactose metabolism, colostrum immunity, milk protein, and milk fat by functional enrichment analysis. Through the combined analysis between 28 candidate genes and transcriptome data of the sow mammary gland, we found nine commons (ANO3, MUC15, DISP3, FBXO6, CLCN6, HLA-DRA, SLA-DRB1, SLA-DQB1, and SLA-DQA1). Furthermore, by comparing the chromosome positions of the candidate genes with the quantitative trait locus (QTLs) as previously reported, a total of 17 genes were found to be within 0.86–94.02 Mb of the reported QTLs for sow milk production ability, in which, NAV2 was found to be located with 0.86 Mb of the QTL region ssc2: 40936355. In conclusion, we identified seven significant SNPs located on SSC2, 6, 7, 10, 11, and 15, and propose 28 candidate genes for the ability to produce milk in Yorkshire pigs, 10 of which were key candidates.
Collapse
Affiliation(s)
- Lijun Shi
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yang Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qian Liu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Longchao Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ligang Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xin Liu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongmei Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xinhua Hou
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hua Yan
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lixian Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| |
Collapse
|
2
|
Sun R, Weng H, Wang MH. W-Test for Genetic Epistasis Testing. Methods Mol Biol 2021; 2212:45-53. [PMID: 33733349 DOI: 10.1007/978-1-0716-0947-7_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The genetic epistasis effect has been widely acknowledged as an essential contributor to genetic variation in complex diseases. In this chapter, we introduce a powerful and efficient statistical method, called W-test, for genetic epistasis testing. A wtest R package is developed for the implementation of the W-test method, which provides various functions to measure the main effect, pairwise interaction, higher-order interaction, and cis-regulation of SNP-CpG pairs in genetic and epigenetic data. It allows flexible stagewise and exhaustive association testing as well as diagnostic checking on the probability distributions in a user-friendly interface. The wtest package is available in CRAN at https://CRAN.R-project.org/package=wtest .
Collapse
Affiliation(s)
- Rui Sun
- The Chinese University of Hong Kong, Hong Kong, China
| | - Haoyi Weng
- The Chinese University of Hong Kong, Hong Kong, China
| | | |
Collapse
|
3
|
Sun R, Xia X, Chong KC, Zee BCY, Wu WKK, Wang MH. wtest: an integrated R package for genetic epistasis testing. BMC Med Genomics 2019; 12:180. [PMID: 31874630 PMCID: PMC6929460 DOI: 10.1186/s12920-019-0638-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 11/26/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND With the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis. The identification of SNP-SNP, SNP-CpG, and higher order interactions helps explain the genetic etiology of human diseases, yet genome-wide analysis for interactions has been very challenging, due to the computational burden and a lack of statistical power in most datasets. RESULTS The wtest R package performs association testing for main effects, pairwise and high order interactions in genome-wide association study data, and cis-regulation of SNP and CpG sites in genome-wide and epigenome-wide data. The software includes a number of post-test diagnostic and analysis functions and offers an integrated toolset for genetic epistasis testing. CONCLUSIONS The wtest is an efficient and powerful statistical tool for integrated genetic epistasis testing. The package is available in CRAN: https://CRAN.R-project.org/package=wtest.
Collapse
Affiliation(s)
- Rui Sun
- Division of Biostatistics and Centre for Clinical Research and Biostatistics(CCRB), JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.,Centre for Clinical Trials and Biostatistics, CUHK Shenzhen Research Institute, Shenzhen, China
| | - Xiaoxuan Xia
- Division of Biostatistics and Centre for Clinical Research and Biostatistics(CCRB), JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.,Centre for Clinical Trials and Biostatistics, CUHK Shenzhen Research Institute, Shenzhen, China
| | - Ka Chun Chong
- Division of Biostatistics and Centre for Clinical Research and Biostatistics(CCRB), JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.,Centre for Clinical Trials and Biostatistics, CUHK Shenzhen Research Institute, Shenzhen, China
| | - Benny Chung-Ying Zee
- Division of Biostatistics and Centre for Clinical Research and Biostatistics(CCRB), JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.,Centre for Clinical Trials and Biostatistics, CUHK Shenzhen Research Institute, Shenzhen, China
| | - William Ka Kei Wu
- Institute of Digestive Diseases and Department of Medicine & Therapeutics, State Key Laboratory of Digestive Diseases, LKS Institute of Health Sciences, CUHK Shenzhen Research Institute, Shenzhen, China.,Department of Anesthesia, the Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China
| | - Maggie Haitian Wang
- Division of Biostatistics and Centre for Clinical Research and Biostatistics(CCRB), JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China. .,Centre for Clinical Trials and Biostatistics, CUHK Shenzhen Research Institute, Shenzhen, China.
| |
Collapse
|
4
|
de Andrade M, Warwick Daw E, Kraja AT, Fisher V, Wang L, Hu K, Li J, Romanescu R, Veenstra J, Sun R, Weng H, Zhou W. The challenge of detecting genotype-by-methylation interaction: GAW20. BMC Genet 2018; 19:81. [PMID: 30255819 PMCID: PMC6157121 DOI: 10.1186/s12863-018-0650-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND GAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. GAW20 distributed real data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, including single-nucleotide polymorphism (SNP) markers, methylation (cytosine-phosphate-guanine [CpG]) markers, and phenotype information on up to 995 individuals. In addition, a simulated data set based on the real data was provided. RESULTS The 7 contributed papers analyzed these data sets with a number of different statistical methods, including generalized linear mixed models, mediation analysis, machine learning, W-test, and sparsity-inducing regularized regression. These methods generally appeared to perform well. Several papers confirmed a number of causative SNPs in either the large number of simulation sets or the real data on chromosome 11. Findings were also reported for different SNPs, CpG sites, and SNP-CpG site interaction pairs. CONCLUSIONS In the simulation (200 replications), power appeared generally good for large interaction effects, but smaller effects will require larger studies or consortium collaboration for realizing a sufficient power.
Collapse
Affiliation(s)
- Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, MN 55905 USA
| | - E. Warwick Daw
- Division of Statistical Genomics, Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 Euclid Ave, Saint Louis, MO 63110 USA
| | - Aldi T. Kraja
- Division of Statistical Genomics, Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 Euclid Ave, Saint Louis, MO 63110 USA
| | - Virginia Fisher
- Department of Biostatistics, Boston University School of Public Health, Boston, 715 Albany St, Boston, MA 02118 USA
| | - Lan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, 715 Albany St, Boston, MA 02118 USA
| | - Ke Hu
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106 USA
| | - Jing Li
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106 USA
| | - Razvan Romanescu
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, University of Toronto, 600 University Ave, Toronto, ON M5G 1X5 Canada
| | - Jenna Veenstra
- Department of Biology, Dordt College, 498 4th Ave. NE, Sioux Center, IA 51250 USA
- Department of Mathematics and Statistics, Dordt College, 498 4th Ave. NE, Sioux Center, IA 51250 USA
| | - Rui Sun
- Division of Biostatistics, Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T, Hong Kong, SAR China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Haoyi Weng
- Division of Biostatistics, Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T, Hong Kong, SAR China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Wenda Zhou
- Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027 USA
| |
Collapse
|