A simple genome-wide association study algorithm
This work addresses computational bottlenecks in GWAS for genetic researchers, but it appears incremental as it builds on existing methods with a focus on simplicity rather than major breakthroughs.
The authors tackled the computational complexity of genome-wide association studies by proposing a simple algorithm that estimates main and epistatic effects of SNPs by considering pairs of individuals rather than SNPs, which reduces dependency on the number of SNPs and primarily depends on the number of individuals, with numerical experiments on real data sets demonstrating its feasibility.
A computationally simple genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that changes of alleles corresponding to important SNPs in a pair of individuals lead to large difference of phenotype values of these individuals. The algorithm is based on considering pairs of individuals instead of SNPs or pairs of SNPs. The main advantage of the algorithm is that it weakly depends on the number of SNPs in a genotype matrix. It mainly depends on the number of individuals, which is typically very small in comparison with the number of SNPs. Numerical experiments with real data sets illustrate the proposed algorithm.