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Li MK, Yuan YX, Zhu B, Wang KW, Fung WK, Zhou JY. Gene-Based Methods for Estimating the Degree of the Skewness of X Chromosome Inactivation. Genes (Basel) 2022; 13:genes13050827. [PMID: 35627212 PMCID: PMC9140558 DOI: 10.3390/genes13050827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/01/2022] [Accepted: 05/02/2022] [Indexed: 11/16/2022] Open
Abstract
Skewed X chromosome inactivation (XCI-S) has been reported to be associated with some X-linked diseases, and currently several methods have been proposed to estimate the degree of the XCI-S (denoted as γ) for a single locus. However, no method has been available to estimate γ for genes. Therefore, in this paper, we first propose the point estimate and the penalized point estimate of γ for genes, and then derive its confidence intervals based on the Fieller’s and penalized Fieller’s methods, respectively. Further, we consider the constraint condition of γ∈[0, 2] and propose the Bayesian methods to obtain the point estimates and the credible intervals of γ, where a truncated normal prior and a uniform prior are respectively used (denoted as GBN and GBU). The simulation results show that the Bayesian methods can avoid the extreme point estimates (0 or 2), the empty sets, the noninformative intervals ([0, 2]) and the discontinuous intervals to occur. GBN performs best in both the point estimation and the interval estimation. Finally, we apply the proposed methods to the Minnesota Center for Twin and Family Research data for their practical use. In summary, in practical applications, we recommend using GBN to estimate γ of genes.
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Affiliation(s)
- Meng-Kai Li
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Yu-Xin Yuan
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Bin Zhu
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Kai-Wen Wang
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Wing Kam Fung
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China;
| | - Ji-Yuan Zhou
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
- Correspondence:
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