A mixture distribution approach for assessing genetic impact from twin study
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916849753522176 |
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| author | Hu, Zonghui Li, Pengfei Follmann, Dean Qin, Jing |
| author_facet | Hu, Zonghui Li, Pengfei Follmann, Dean Qin, Jing |
| contents | This work was motivated by a twin study with the goal of assessing the genetic control of immune traits. We propose a mixture bivariate distribution to model twin data where the underlying order within a pair is unclear. Though estimation from mixture distribution is usually subject to low convergence rate, the combined likelihood, which is constructed over monozygotic and dizygotic twins combined, reaches root-n consistency and allows effective statistical inference on the genetic impact. The method is applicable to general unordered pairs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13605 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A mixture distribution approach for assessing genetic impact from twin study Hu, Zonghui Li, Pengfei Follmann, Dean Qin, Jing Methodology Applications This work was motivated by a twin study with the goal of assessing the genetic control of immune traits. We propose a mixture bivariate distribution to model twin data where the underlying order within a pair is unclear. Though estimation from mixture distribution is usually subject to low convergence rate, the combined likelihood, which is constructed over monozygotic and dizygotic twins combined, reaches root-n consistency and allows effective statistical inference on the genetic impact. The method is applicable to general unordered pairs. |
| title | A mixture distribution approach for assessing genetic impact from twin study |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2507.13605 |