A mixture distribution approach for assessing genetic impact from twin study

Fuente: arXiv
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Main Authors: Hu, Zonghui, Li, Pengfei, Follmann, Dean, Qin, Jing
Format: Preprint
Published: 2025
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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