The application of the sum of single effects regression model for colocalization analysis in multi-omics data

HUANG Ting; LIU Jincheng; LI Huilin; WU Yiwen; YU Er; JI Kai; TANG Shaowen; ZHAO Yang; DAI Juncheng; YI Honggang

doi:10.16462/j.cnki.zhjbkz.2024.01.019

Volume 28 Issue 1

Jan. 2024

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2024 > 28(1): 117-121

HUANG Ting, LIU Jincheng, LI Huilin, WU Yiwen, YU Er, JI Kai, TANG Shaowen, ZHAO Yang, DAI Juncheng, YI Honggang. The application of the sum of single effects regression model for colocalization analysis in multi-omics data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 117-121. doi: 10.16462/j.cnki.zhjbkz.2024.01.019

Citation:

HUANG Ting, LIU Jincheng, LI Huilin, WU Yiwen, YU Er, JI Kai, TANG Shaowen, ZHAO Yang, DAI Juncheng, YI Honggang. The application of the sum of single effects regression model for colocalization analysis in multi-omics data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 117-121. doi: 10.16462/j.cnki.zhjbkz.2024.01.019

Citation:

HUANG Ting, LIU Jincheng, LI Huilin, WU Yiwen, YU Er, JI Kai, TANG Shaowen, ZHAO Yang, DAI Juncheng, YI Honggang. The application of the sum of single effects regression model for colocalization analysis in multi-omics data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 117-121. doi: 10.16462/j.cnki.zhjbkz.2024.01.019

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The application of the sum of single effects regression model for colocalization analysis in multi-omics data

doi: 10.16462/j.cnki.zhjbkz.2024.01.019

1.
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
2.
Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
3.
Key Laboratory of Biomedical Big Data, Cancer Individualized Medicine Collaborative Innovation Center, Nanjing Medical University, Nanjing 211166, China

Funds:

National Natural Science Foundation of China 81941020

College Student Innovation and Entrepreneurship Training Program 202210312151

More Information

Corresponding author: YI Honggang, E-mail: honggangyi@njmu.edu.cn
Received Date: 2022-12-15
Rev Recd Date: 2023-03-23

Available Online: 2024-02-05

Publish Date: 2024-01-10

Abstract

Abstract

Objective To explore the application of the sum of single effects (SuSiE) regression model for colocalization analysis with multi-omics data. Methods Taking the simulated data as an example, we introduced the basic principle of SuSiE regression model and the statistical analysis procedures using R software. Results The results showed that the SuSiE regression model could identify the shared casual variants as associated with traits through taking account the linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs). Despite the presence of multiple causal variants, the colocalization results were still stable. Conclusions Compared with those traditional approaches for colocalization, SuSiE regression model expands the applicability of the single causal variant hypothesis and it has higher computational efficiency, thus helping to detect multiple potential shared casual variants using multi-omics data.
- Multi-omics,
- Colocalization,
- Approximate Bayes factor,
- Casual variants

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References(13)

References

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