畜牧兽医学报 ›› 2020, Vol. 51 ›› Issue (8): 1804-1810.doi: 10.11843/j.issn.0366-6964.2020.08.004

• 遗传育种 • 上一篇    下一篇

大型基因组亲缘矩阵求逆算法的优化研究

周洁1, 曾维俊1, 杨天瑞1, 程郁斐1, 龙贤达1, 经佩齐1, 曾仰双2, 徐旭2, 唐国庆1*   

  1. 1. 四川农业大学, 成都 611130;
    2. 四川省畜牧总站, 成都 610041
  • 收稿日期:2020-01-20 出版日期:2020-08-25 发布日期:2020-08-19
  • 通讯作者: 唐国庆,主要从事猪分子数量遗传学研究,E-mail:tyq003@163.com
  • 作者简介:周洁(1995-),男,重庆丰都人,硕士生,主要从事猪的遗传育种研究,E-mail:1048185949@qq.com
  • 基金资助:
    四川省科技计划项目(20ZDYF1241);四川生猪创新团队(SCSZTD-3-002);国家生猪产业技术体系项目(CARS-36-01A)

Optimization Study of Inverse Algorithm of Large Genomic Relationship Matrix

ZHOU Jie1, ZENG Weijun1, YANG Tianrui1, CHENG Yufei1, LONG Xianda1, JING Peiqi1, ZENG Yangshuang2, XU Xu2, TANG Guoqing1*   

  1. 1. Sichuan Agricultural University, Chengdu 611130, China;
    2. Sichuan Animal Husbandry Station, Chengdu 610041, China
  • Received:2020-01-20 Online:2020-08-25 Published:2020-08-19

摘要: 基因组选择常用的评估方法GBLUP和ssGBLUP都涉及到基因组亲缘矩阵的求逆,而大规模矩阵求逆运算非常耗时。本研究以提高大型基因组亲缘矩阵求逆运算的效率为目的。本研究通过真实数据和模拟数据构建基因组亲缘矩阵,引入Intel MKL矩阵函数,以减少迭代次数(方法1)和重复分块(方法2)两种方式改良分块迭代求逆算法,编程实现算法并在台式电脑和服务器上测试计算时间。结果表明,利用方法1计算4 000×4 000的基因组亲缘矩阵逆矩阵时,与MKL库函数的加速比为0.898。而16 000×16 000矩阵的计算速度为MKL库函数的1.006倍。利用方法2计算4 000×4 000矩阵的运算速度是MKL库函数的1.084倍;而在更大型的128 000×128 000基因组亲缘矩阵求逆运算时,该方法与MKL直接求逆函数的加速比为1.805倍。相比于MKL直接求逆函数,改进后的两种方法在效率上有一定程度的提升。

关键词: 基因组选择, 矩阵求逆, 分块迭代求逆

Abstract: Both GBLUP and ssGBLUP methods involved in the inversion of genomic relationship matrices in genomic selection, and large-scale matrix inversion operations were time-consuming. The purpose of this study was to improve the efficiency of the inverse operation of large genomic relationship matrix. The genomic relationship matrix from real data and simulated data was constructed, and the Intel MKL matrix function was introduced, and the efficiency of matrix inversion was improved by reducing the number of iterations (method 1) and repeating the block (method 2), programming to implement algorithms and test computing time on desktop computers and servers. The results showed that the acceleration ratio of the direct inversion function with the MKL was 0.898 when calculating the genomic relationship matrix of 4 000×4 000 using method 1. The calculation speed of 16 000×16 000 was 1.006 times more than that of the inversion function in MKL; The calculation speed of method 2 in the 4 000×4 000 genomic relationship matrix was 1.084 times more than that of the MKL inversion function; For larger 128 000×128 000 matrix inversion operations, the acceleration ratio of this method was 1.805 times more than that of the inversion function in MKL. Compared with MKL direct inversion function, the improved two methods have higher efficiency.

Key words: genomic selection, matrix inversion, block iterative inverse

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