病例对照研究中人类性状遗传力的估计

IF 1 4区 生物学 Q4 GENETICS & HEREDITY
Die Hu, Shuyue Chen, Hong Zhang
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引用次数: 0

摘要

检测人类复杂性状的遗传缺失性具有重要意义。加性遗传效应(ADD)、母体遗传效应(MGE)和亲本遗传效应(POE)在遗传机制中发挥着重要作用。文献中已经开发了方法来分别分析ADD、POE和MGE的遗传力,而不是同时分析。本文提出了一种基于母子二代遗传数据的APM模型,该模型对ADD、POE和MGE的遗传力进行正交分解。这种正交分解是生物学上可解释的,因为它理想地描述了由于三种效应而产生的独立贡献。本文针对遗传学研究中广泛采用的病例对照数据,通过调整病例对照研究中因抽样偏倚造成的估计偏差,并对遗传力估计施加非负性约束,提出了一种新的R-PCGC方法。建立了R-PCGC的一致性和渐近正态性等大样本性质。通过仿真验证了R-PCGC的理想性质(即渐近无偏和非负性)。最后,将R-PCGC回归应用于丹麦国家出生队列(DNBC)早产儿的病例对照研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of genetic heritabilities of human traits in case–control studies
It is of great interest to detect missing heritability for human complex traits. Additive genetic effects (ADD), maternal genetic effects (MGE), and parent‐of‐origin effects (POE) play important roles in genetic mechanisms. Methods have been developed in the literature to analyze heritabilities due to ADD, POE, and MGE separately but not simultaneously. In this paper, a new model termed APM is proposed based on mother–child duos genetic data, which orthogonally decomposes heritabilities due to ADD, POE, and MGE. This orthogonal decomposition is biologically interpretable since it ideally characterizes independent contributions due to the three effects. We focus on case–control data that are widely adopted in genetics studies and develop a novel method R‐PCGC by adjusting estimation biases due to sampling bias in case–control studies and imposing nonnegative constraints on the heritability estimates. Large sample properties such as consistency and asymptotic normality are established for R‐PCGC. The desired properties of R‐PCGC (i.e., asymptotic unbiasedness and nonnegativity) are confirmed through simulations. Finally, R‐PCGC regression is applied to a case–control study of preterm births from the Danish National Birth Cohort (DNBC).
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来源期刊
Annals of Human Genetics
Annals of Human Genetics 生物-遗传学
CiteScore
4.20
自引率
0.00%
发文量
34
审稿时长
3 months
期刊介绍: Annals of Human Genetics publishes material directly concerned with human genetics or the application of scientific principles and techniques to any aspect of human inheritance. Papers that describe work on other species that may be relevant to human genetics will also be considered. Mathematical models should include examples of application to data where possible. Authors are welcome to submit Supporting Information, such as data sets or additional figures or tables, that will not be published in the print edition of the journal, but which will be viewable via the online edition and stored on the website.
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