Calculating genetic risk scores directly from summary statistics with an application to type 1 diabetes.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2025-07-02 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf158

Steven Squires, Michael N Weedon, Richard A Oram

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引用次数: 0

Abstract

Motivation: Genetic risk scores (GRS) summarise genetic data into a single number and allow for discrimination between cases and controls. Many applications of GRSs would benefit from comparisons with multiple datasets to assess quality of the GRS across different groups. However, genetic data is often unavailable. If summary statistics of the genetic data could be used to calculate GRSs more comparisons could be made, potentially leading to improved research.

Results: We present a methodology that utilises only summary statistics of genetic data to calculate GRSs with an example of a type 1 diabetes (T1D) GRS. An example on European populations of the mean T1D GRS for those calculated from genetic data and from summary statistics (our method) was 10.31 (10.12-10.48) and 10.38 (10.24-10.53), respectively. An example of a case-control set for T1D has an area under the receiver operating characteristic curve of 0.917 (0.903-0.93) for those calculated from genetic data and 0.914 (0.898-0.929) for those calculated from summary statistics.

Availability: The code is available at https://github.com/stevensquires/simulating_genetic_risk_scores.

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计算遗传风险得分直接从汇总统计与应用于1型糖尿病。

动机：遗传风险评分（GRS）将遗传数据汇总为一个数字，并允许在病例和对照组之间进行区分。GRS的许多应用将受益于与多个数据集的比较，以评估不同群体的GRS质量。然而，基因数据往往是不可用的。如果遗传数据的汇总统计可以用来计算grs，就可以进行更多的比较，从而有可能改进研究。结果：我们提出了一种方法，仅利用遗传数据的汇总统计来计算1型糖尿病（T1D） GRS的例。以欧洲种群为例，遗传数据和汇总统计的平均T1D GRS分别为10.31（10.12-10.48）和10.38（10.24-10.53）。以T1D病例-对照集为例，遗传数据计算的受试者工作特征曲线下面积为0.917(0.903-0.93)，汇总统计计算的受试者工作特征曲线下面积为0.914（0.898-0.929）。可用性：代码可在https://github.com/stevensquires/simulating_genetic_risk_scores上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

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