College of Life Science, Northeast Agricultural University, Harbin 150030, China.
College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China.
J Hered. 2022 Jul 23;113(4):472-478. doi: 10.1093/jhered/esac006.
R/glmnet has been successfully applied to jointly mapped multiple quantitative trait loci for linkage analysis, along with statistical inference for quantitative trait loci candidates with nonzero genetic effects using R/lm for normally distributed traits, R/glm for discrete traits, and R/coxph for survival times. In this study, we extended R/glmnet to a genome-wide association study by means of parallel computation. A multi-locus genome-wide association study for high-throughput single-nucleotide polymorphisms was implemented in the "Multi-Runking" software written within the R workspace. This software can better detect common and large quantitative trait nucleotides and more accurately estimate than genome-wide mixed model analysis for one single-nucleotide polymorphism at a time and linear mixed models-least absolute shrinkage and selection operator. Its applicability and utility were demonstrated by multi-locus genome-wide association studies for the simulated and real traits distributed normally, binary traits, and survival times.
R/glmnet 已成功应用于联合映射多个数量性状基因座进行连锁分析，并使用 R/lm 对正态分布性状、R/glm 对离散性状和 R/coxph 对生存时间进行具有非零遗传效应的数量性状基因座候选物进行统计推断。在这项研究中，我们通过并行计算将 R/glmnet 扩展到全基因组关联研究中。在 R 工作空间内编写的“Multi-Runking”软件中实现了高通量单核苷酸多态性的多基因座全基因组关联研究。该软件可以更好地检测常见和大的数量性状核苷酸，并比每次全基因组混合模型分析和线性混合模型-最小绝对收缩和选择算子更准确地估计。通过对正态分布、二项性状和生存时间的模拟和真实性状的多基因座全基因组关联研究，证明了其适用性和实用性。
