Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
文献类型：期刊论文
| 作者 | Li,Xin1; Wu,Dongya2,3,7 ; Cui,Yue2,3; Liu,Bing2,3; Walter,Henrik8; Schumann,Gunter9; Li,Chong1; Jiang,Tianzi2,3,4,5,6,7
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| 刊名 | BMC Bioinformatics
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| 出版日期 | 2019-04-30 |
| 卷号 | 20期号:1 |
| 关键词 | Heritability Reliable estimation Sparse regularization Standard error Simulation |
| ISSN号 | 1471-2105 |
| DOI | 10.1186/s12859-019-2792-7 |
| 通讯作者 | Jiang,Tianzi(jiangtz@nlpr.ia.ac.cn) |
| 英文摘要 | AbstractBackgroundData from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability.ResultsIn this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy.ConclusionsThe proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era. |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S12859-019-2792-7 |
| 出版者 | BioMed Central |
| 源URL | [http://ir.ia.ac.cn/handle/173211/24461]  |
| 专题 | 自动化研究所_脑网络组研究中心 |
| 通讯作者 | Jiang,Tianzi |
| 作者单位 | 1. 2. 3. 4. 5. 6. 7. 8. 9. |
| 推荐引用方式 GB/T 7714 | Li,Xin,Wu,Dongya,Cui,Yue,et al. Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies[J]. BMC Bioinformatics,2019,20(1). |
| APA | Li,Xin.,Wu,Dongya.,Cui,Yue.,Liu,Bing.,Walter,Henrik.,...&Jiang,Tianzi.(2019).Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies.BMC Bioinformatics,20(1). |
| MLA | Li,Xin,et al."Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies".BMC Bioinformatics 20.1(2019). |
入库方式： OAI收割
来源：自动化研究所
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