用于祖先特异性关联研究的异质性感知整合回归。

IF 1.4 4区 数学 Q3 BIOLOGY Biometrics Pub Date : 2024-10-03 DOI:10.1093/biomtc/ujae109
Aaron J Molstad, Yanwei Cai, Alexander P Reiner, Charles Kooperberg, Wei Sun, Li Hsu
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引用次数: 0

摘要

基于基因预测蛋白表达的特定祖先全蛋白质组关联研究(PWAS)可以揭示某些祖先群体特有的复杂疾病病因。这些研究需要特定祖先的蛋白质表达模型作为 SNP 基因型的函数。为了改善在基因组研究中历来代表性不足的祖先人群的蛋白质表达预测,我们提出了一种新的惩罚性最大似然估计器,用于拟合祖先特异性联合蛋白质数量性状位点模型。我们的估计器借用了不同祖先群体的信息,同时允许异质性误差方差和回归系数。我们提出了模型的另一种参数化方法,使目标函数具有凸性和惩罚尺度不变性。为了提高计算效率,我们提出了一种近似版本的方法,并对其理论特性进行了研究。我们的方法大大提高了非洲血统个体蛋白质表达预测的准确性,并在下游的 PWAS 分析中发现了非洲血统人群中蛋白质表达与血脂特征之间的多种关联。
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Heterogeneity-aware integrative regression for ancestry-specific association studies.

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.

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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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