A mixture distribution approach for assessing genetic impact from twin study

TL;DR

This paper introduces a mixture distribution model for twin data to accurately assess genetic influence on immune traits, overcoming estimation challenges and enabling effective inference for unordered twin pairs.

Contribution

It proposes a novel mixture bivariate distribution approach that handles unordered twin data, providing consistent estimation of genetic effects.

Findings

Achieves root-n consistency in estimation.

Applicable to general unordered twin pairs.

Enables effective genetic impact assessment.

Abstract

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.