OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices

@inproceedings{Xiong2019OCMAFM,
  title={OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices},
  author={Zhi Xiong and Qingrun Zhang and Alexander Platt and Wenyuan Liao and Xinghua Shi and Gustavo de Los Campos and Quan Long},
  booktitle={G3},
  year={2019}
}
Matrices representing genetic relatedness among individuals (i.e., Genomic Relationship Matrices, GRMs) play a central role in genetic analysis. The eigen-decomposition of GRMs (or its alternative that generates fewer top singular values using genotype matrices) is a necessary step for many analyses including estimation of SNP-heritability, Principal Component Analysis (PCA), and genomic prediction. However, the GRMs and genotype matrices provided by modern biobanks are too large to be stored… CONTINUE READING
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