KMC2D: GPU Empowered Pipeline for Kinship Matrix Calculation-2D Epistatic Effect
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 Kinship Matrix, Epistatic(2D) Kinship Matrix and GPU Empowered Epistatic (2D) Kinship Matrix Calculating In population genetics, the Kinship matrix is relatedness matrix, which is always used to measure the degree of relationship between any of two related individuals. Suppose there are n individuals, and each individual is represented explicitly by a node of pedigree tree or by a large number of genotypic markers(m). The kinship matrix K is a 2D matrix with the dimension of nxn . We can use the genotypic marker to generate a kinship and its calculation is a very critical step in genome-wide association studies(GWASs). Here, the kinship matrix entry K(i,j) is a coefficient to assess the genetic resemblance between individual i and individual j. Such a Kinship matrix is called the main effect (1D) kinship matrix when compared to our defined 2D epistatic effect (marker pair) kinship matrix. Consider the symmetry, the kinship matrix is a diagonal matrix. Xu et al. proposed a new polygenic Linear Mixed Model(LMM) for epistatic effect GWAS analysis. To solve the polygenic LMM, 4 kinds of epistatic kinship matrix and the formulas to use the marker pairs to calculate the epistatic kinship matrix were mathematically defined. Suppose there are m markers, the marker pair number is C(m,2)=m(m-1)/2 . Therefore, compared with the direct marker-assist main effect(1D) kinship matrix, the epistatic kinship matrix calculations are particular very time-consuming. In the recent years, GPU (Graphics Processing Units) with multiple hardware processor (>1,000) cores has become a standard HPC (High Performance Computing) solution system for large scale computing, e.g. large scale matrix operations. We have analyzed the math principle and the complexity of marker-pair assist epistatic kinship matrix, and successfully developed this GPU empowered pipeline,KMC2D , for epistatic effect kinship matrix calculation. Briefly, we first divide the ultra-high-dimensional marker pairs into successive blocks. We then calculate the kinship matrix for each block and merge the block-wise kinship matrices to form the genome-wide kinship matrix. All the matrix operations have been parallelized using GPU kernels on our NVIDIA GPU-accelerated server platform. Our performance analyses show that the calculation speed of KMC2D can be accelerated by several hundred times over the conventional CPU-based computing. The user are required to upload the two kinds of genotype matrix files for computing one of the four epistatic kinship matrix, e.g., additive-additive , additive-dominance, dominance-additive, or dominance-dominance, respectively. To calculate the main effect 1D kinship matrix, you may use our other GPU pipeline KMC1D. Reference: 1. Xu, S., "Mapping Quantitative Trait Loci by Controlling Polygenic Background Effects". Genetics, 2013. 195:1209-1222. 2. Zhang W., Dai X., Wang Q., Xu S., Zhao P.X., "PEPIS: A Pipeline for Estimating Epistatic Effects in Quantitative Trait Locus Mapping and Genome-Wide Association Studies", 2016. PLoS Comput Biol, 12(5) 3. Cecilia J. M. , Garc´ıa J. M. , and Ujaldon M., “The GPU on the Matrix-Matrix Multiply: Performance Study and Contributions”, in Parallel Computing: From Multicores and GPU’s to Petascale, B. Chapman et al., Eds. Advances in Parallel Computing, vol. 19, pp. 331-340, 2010. 4. Dobravec T., Bulic P., "Comparing CPU and GPU Implementations of a Simple Matrix Multiplication Algorithm", IJCEE, vol 9, 430-438, 2017.