Matrix sketching framework for linear mixed models in association studies

IF 6.2 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Genome research Pub Date : 2024-09-04 DOI:10.1101/gr.279230.124

Myson C Burch, Aritra Bose, Gregory Dexter, Laxmi Parida, Petros Drineas

引用次数: 0

Abstract

Linear mixed models (LMMs) have been widely used in genome-wide association studies (GWAS) to control for population stratification and cryptic relatedness. However, estimating LMM parameters is computationally expensive, necessitating large-scale matrix operations to build the genetic relatedness matrix (GRM). Over the past 25 years, Randomized Linear Algebra has provided alternative approaches to such matrix operations by leveraging matrix sketching, which often results in provably accurate fast and efficient approximations. We leverage matrix sketching to develop a fast and efficient LMM method called Matrix-Sketching LMM (MaSk-LMM) by sketching the genotype matrix to reduce its dimensions and speed up computations. Our framework comes with both theoretical guarantees and a strong empirical performance compared to current state-of-the-art for simulated traits and complex diseases.

查看原文本刊更多论文

关联研究中线性混合模型的矩阵草图框架

线性混合模型（LMM）已被广泛用于全基因组关联研究（GWAS），以控制种群分层和隐性亲缘关系。然而，估计 LMM 参数的计算成本很高，需要进行大规模的矩阵运算才能建立遗传相关性矩阵（GRM）。在过去的 25 年中，随机线性代数利用矩阵草图为此类矩阵运算提供了替代方法，而矩阵草图通常可以得到准确、快速、高效的近似值。我们利用矩阵素描开发了一种快速高效的 LMM 方法，称为矩阵素描 LMM（MaSk-LMM），通过素描基因型矩阵来减少其维度并加快计算速度。在模拟性状和复杂疾病方面，与目前最先进的方法相比，我们的框架既有理论保证，又有强大的经验性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Genome research 生物-生化与分子生物学

CiteScore

12.40

自引率

1.40%

发文量

140

审稿时长

6 months

期刊介绍： Launched in 1995, Genome Research is an international, continuously published, peer-reviewed journal that focuses on research that provides novel insights into the genome biology of all organisms, including advances in genomic medicine. Among the topics considered by the journal are genome structure and function, comparative genomics, molecular evolution, genome-scale quantitative and population genetics, proteomics, epigenomics, and systems biology. The journal also features exciting gene discoveries and reports of cutting-edge computational biology and high-throughput methodologies. New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are presented electronically on the journal''s web site where appropriate. The journal also provides Reviews, Perspectives, and Insight/Outlook articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context.