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确定群体抽样的亲子三联体的因果基因型-表型关系。

Identifying causal genotype-phenotype relationships for population-sampled parent-child trios.

作者信息

Tang Yushi, Cabreros Irineo, Storey John D

机构信息

Lewis-Sigler Institute for Integrative Genomics, Princeton University, NJ 08544, USA.

Program in Applied and Computational Mathematics, Princeton University, NJ 08544, USA.

出版信息

bioRxiv. 2024 Dec 11:2024.12.10.627752. doi: 10.1101/2024.12.10.627752.

DOI:10.1101/2024.12.10.627752
PMID:39713318
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11661140/
Abstract

The process by which genes are transmitted from parent to child provides a source of randomization preceding all other factors that may causally influence any particular child phenotype. Because of this, it is natural to consider genetic transmission as a source of experimental randomization. In this work, we show how parent-child trio data can be leveraged to identify causal genetic loci by modeling the randomization during genetic transmission. We develop a new test, the transmission mean test (TMT), together with its unbiased estimator of the average causal effect, and derive its causal properties within the potential outcomes framework. We also prove that the transmission disequilibrium test (TDT) is a test of causality as a complementary case of the TMT for the affected-only design. The TMT and the TDT differ in the types of traits that they can handle and the study designs for which they are appropriate. The TMT handles arbitrarily distributed traits and is appropriate when trios are randomly sampled; the TDT handles dichotomous traits and is appropriate when sampling is based on a child's trait status. We compare the transmission-based methods with established approaches for genotype-phenotype analyses to clarify conditions appropriate for each method, what conclusions can be drawn by each one, and how these methods can be used together.

摘要

基因从父母传递给子女的过程,在所有可能因果影响任何特定儿童表型的其他因素之前,提供了一种随机化来源。因此,将基因传递视为实验随机化的一个来源是很自然的。在这项工作中,我们展示了如何通过对基因传递过程中的随机化进行建模,利用亲子三联体数据来识别因果基因位点。我们开发了一种新的检验方法——传递均值检验(TMT),以及其对平均因果效应的无偏估计量,并在潜在结果框架内推导了它的因果性质。我们还证明了传递不平衡检验(TDT)作为TMT针对仅受影响设计的补充情形,是一种因果检验。TMT和TDT在它们能够处理的性状类型以及适用的研究设计方面有所不同。TMT可处理任意分布的性状,在三联体随机抽样时适用;TDT可处理二分性状,在基于儿童性状状态进行抽样时适用。我们将基于传递的方法与用于基因型 - 表型分析的既定方法进行比较,以阐明每种方法适用的条件、每种方法能得出什么结论,以及这些方法如何一起使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/6a667527b394/nihpp-2024.12.10.627752v1-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/8c6da057ead4/nihpp-2024.12.10.627752v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/81a2e2529577/nihpp-2024.12.10.627752v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/81f0ac836205/nihpp-2024.12.10.627752v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/76b223c95e4a/nihpp-2024.12.10.627752v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/c76f0ec79546/nihpp-2024.12.10.627752v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/6a667527b394/nihpp-2024.12.10.627752v1-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/8c6da057ead4/nihpp-2024.12.10.627752v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/81a2e2529577/nihpp-2024.12.10.627752v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/81f0ac836205/nihpp-2024.12.10.627752v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/76b223c95e4a/nihpp-2024.12.10.627752v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/c76f0ec79546/nihpp-2024.12.10.627752v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f2d/11661140/6a667527b394/nihpp-2024.12.10.627752v1-f0006.jpg

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